# LPAI

Table of Contents.

- [API Specializations](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#api-specializations)
- [QNN LPAI Supported Operations](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#qnn-lpai-supported-operations)
- [QNN LPAI Overview](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#qnn-lpai-overview)
- [QNN LPAI Quick Start Guide](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#qnn-lpai-quick-start-guide)
- - [QNN LPAI Setup & Configuration](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#qnn-lpai-setup-configuration)
    - - [Set up the environment variables](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend_setup_configuration.html#qnn-lpai-env-setup)
    - - [Prepare Json Configuration files](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend_setup_configuration.html#qnn-json-prepare)
    - - [QNN LPAI Backend Configuration Guide](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend_setup_configuration.html#qnn-lpai-configuration-guide)
        - [QNN LPAI Backend Configuration Parameters](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend_setup_configuration.html#qnn-lpai-configuration-parameters)
- - [QNN LPAI Model Generation](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#id3)
    - - [Compile LPAI Graph on x86 Linux OS](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#compile-lpai-graph-on-x86-linux-os)
    - [Compile LPAI Graph on x86 Windows OS](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#compile-lpai-graph-on-x86-windows-os)
- - [QNN LPAI Execution](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#id4)
    - - - [QNN LPAI Backend Simulation](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend_model_execution.html#qnn-lpai-sim-execution)
    - - [QNN LPAI Simulation on Linux x86](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#qnn-lpai-simulation-on-linux-x86)
        - [QNN LPAI Simulation on Windows x86](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#qnn-lpai-simulation-on-windows-x86)
        - [:ref:`QNN LPAI Simulation on Hexagon <hexagon_sim_execution>`\_](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#id1)
    - [QNN LPAI ARM Backend Type](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_execution_arm_tutorial.html#qnn-lpai-fastrpc-backend-type)
    - [QNN LPAI Native aDSP Backend Type](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_execution_direct_tutorial.html#qnn-lpai-direct-mode-backend-type)
    - [QNN LPAI Native aDSP Backend Type (Windows on ARM)](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend_model_execution.html#qnn-lpai-direct-mode-backend-type-winarm)
- - [QNN LPAI Profiling](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#id12)
    - - [Profiling Initialization](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#profiling-initialization)
    - [Basic Profiling](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#basic-profiling)
    - [Detailed Profiling](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#detailed-profiling)
    - [Enable Profiling in qnn-net-run](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#enable-profiling-in-qnn-net-run)
    - [Visualize Profile Data with qnn-profile-viewer](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#visualize-profile-data-with-qnn-profile-viewer)
- - [QNN LPAI Performance Infrastructure](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#qnn-lpai-performance-infrastructure)
    - - [Performance Modes](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#performance-modes)
    - [API Usage](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#api-usage)
    - [Performance Profile in qnn-net-run](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#performance-profile-in-qnn-net-run)
- - [QNN LPAI Integration](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#qnn-lpai-integration)
    - - - [QNN LPAI Memory Management](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend_memory_allocations.html#qnn-lpai-memory-management)
    - - [TCM Memory Support in LPAIBackendExtensions (ADSP Direct Mode)](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend_memory_allocations.html#qnn-lpai-tcm-memory-support)
        - [Testing TCM Memory Support](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend_memory_allocations.html#qnn-lpai-tcm-testing)
    - [QNN LPAI Data Structures and Enumerations](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend_integration.html#qnn-lpai-data-structures-enums)
- - [QNN API Call Flow](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#id36)
    - - [Initialization](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#initialization)
    - [Execution](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#execution)
    - [Deinitialization](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#deinitialization)
- [QNN LPAI Shared Buffer Tutorial](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#id37)
- [`QNN LPAI Graph Pause and Resume Tutorial`\_](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#id85)
- [`Troubleshooting for QNN LPAI Backends (x86 Simulator, Hexagon Simulator, ARM & aDSP)`\_](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#id87)
- [Troubleshooting Table](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#troubleshooting-table)
- [QNN LPAI Backend FAQs](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#qnn-lpai-backend-faqs)
- [QNN LPAI Backend Glossary](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#qnn-lpai-backend-glossary)

## API Specializations

This section contains information related to API specialization for the LPAI backend.
All QNN LPAI backend specializations are available under the `<QNN_SDK_ROOT>/include/QNN/LPAI/` directory.

The current version of the QNN LPAI backend API is:

Warning

doxygendefine: Cannot find define “QNN\_LPAI\_API\_VERSION\_MAJOR” in doxygen xml output for project “QairtCApi” from directory: /local/mnt/workspace/mlg\_user\_admin/ci.docker.tmp/62\_e394c/build/x86\_64-linux-clang/FirstParty/QNN/Doc/qairt-api-docs/c-api-docs/xml

Warning

doxygendefine: Cannot find define “QNN\_LPAI\_API\_VERSION\_MINOR” in doxygen xml output for project “QairtCApi” from directory: /local/mnt/workspace/mlg\_user\_admin/ci.docker.tmp/62\_e394c/build/x86\_64-linux-clang/FirstParty/QNN/Doc/qairt-api-docs/c-api-docs/xml

Warning

doxygendefine: Cannot find define “QNN\_LPAI\_API\_VERSION\_PATCH” in doxygen xml output for project “QairtCApi” from directory: /local/mnt/workspace/mlg\_user\_admin/ci.docker.tmp/62\_e394c/build/x86\_64-linux-clang/FirstParty/QNN/Doc/qairt-api-docs/c-api-docs/xml

## QNN LPAI Supported Operations

QNN LPAI supports running quantized 8-bit and quantized 16-bit networks on supported Qualcomm chipsets.
A list of operations supported by the QNN LPAI runtime can be found under the Backend Support LPAI column in
OpDef/SupportedOps:Supported Operations.

## QNN LPAI Overview

LPAI (Low Power AI) is a programmable ML engine optimized for low-area, low-power applications. It is optimized for deeply embedded use cases such as:

- Always-on voice use cases on mobile, XR or IoT platforms.
- Voice and music use cases on IoT platforms
- Voice AI use cases such as Automatic Speech Recognition (ASR), Speech Caption, and etc
- Always-on camera use cases on mobile, XR or IoT platforms
- Qualcomm Sensor hubs

This document provides a user-friendly guide to using the QNN LPAI backend for model generation, execution, result analysis and profiling.

## QNN LPAI Quick Start Guide

Follow these steps to get started quickly:

1. [Setup the environment variables (QNN\_SDK\_ROOT, PATH, LD\_LIBRARY\_PATH)](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend_setup_configuration.html#qnn-lpai-env-setup)
2. [Prepare the JSON configuration file](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend_setup_configuration.html#qnn-json-prepare)
3. [QNN LPAI Model Generation.](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend_model_generation.html#qnn-lpai-model-generation)
4. [Transfer model and input files to the target device](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend_model_execution.html#qnn-lpai-prepare-test-platform)
5. [QNN LPAI Execution](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend_model_execution.html#qnn-lpai-execution)
6. [QNN LPAI Profiling](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend_profiler.html#qnn-lpai-profiling)
7. [Troubleshooting for QNN LPAI Backends (x86 Simulator, ARM & aDSP)](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#qnn-lpai-troubleshooting)

## QNN LPAI Setup & Configuration

**Set up the environment variables**

> 
> 
> Set up your environment with the required SDK paths and configuration files. Use the following variables:
> 
> - This includes setting paths to toolchains, libraries, and runtime binaries.
> - Key environment variables:
> 
>     - `QNN_SDK_ROOT`: Root directory of the QNN SDK installation.
>     - `PATH`: Must include paths to QNN tools and binaries (e.g., `$QNN_SDK_ROOT/bin`).
>     - `LD_LIBRARY_PATH` (Linux only): Must include paths to required shared libraries (e.g., `$QNN_SDK_ROOT/lib`).
> 
> 
> 
> Important
> 
> 
> Ensure the following environment variables are set before using offline tools:
> 
> 
> **Linux Example**:
> 
> 
> export QNN_SDK_ROOT=/path/to/qnn_sdk
>     export PATH=$QNN_SDK_ROOT/bin/x86_64-linux-clang:$PATH
>     export LD_LIBRARY_PATH=$QNN_SDK_ROOT/lib/x86_64-linux-clang:$LD_LIBRARY_PATH
>     Copy to clipboard
> 
> 
> **Windows Example (Command Prompt)**:
> 
> 
> set QNN_SDK_ROOT=C:\path\to\qnn_sdk
>     set PATH=%QNN_SDK_ROOT%\bin\x86_64-windows-msvc;%PATH%
>     Copy to clipboard

**Prepare the JSON configuration file**

> 
> 
> The configuration file defines both **model generation** and **execution parameters** for a specific LPAI hardware version.
> 
> - The JSON file consists of two sections:
> 
>     - **Model generation**: Specifies how the model should be compiled for the target LPAI version.
>     - **Model execution**: Defines runtime behavior, including memory allocation and device-specific settings.
> - Different Snapdragon platform may support different LPAI versions. Refer to the compatibility table at Supported Snapdragon Devices.
> 
> 
> 
> Create a configuration JSON file with model generation and execution parameters. Example:
> 
> 
> {
>        "lpai_backend": {
>           "target_env": "adsp",
>           "enable_hw_ver": "v6"
>        }
>     }
>     Copy to clipboard
> 
> - For detailed instructions, see the [QNN LPAI Backend Configuration Guide](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#qnn-lpai-backend-configuration-guide).

## QNN LPAI Backend Configuration Guide

- [Overview](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#overview)
- [Configuration Schema](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#configuration-schema)

    - [lpai_backend](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#lpai-backend)
    - [lpai_graph](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#lpai-graph)
    - [lpai_profile (Optional)](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#lpai-profile-optional)
    - [lpai_debug (Optional)](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#lpai-debug-optional)
- [QNN LPAI Backend Configuration Parameters](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#qnn-lpai-backend-configuration-parameters)

    - [Fps and ftrt_ratio information](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#fps-and-ftrt-ratio-information)
    - [Realtime vs Non-Realtime client](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#realtime-vs-non-realtime-client)
    - [Core Selection & Affinity](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#core-selection-affinity)
- [Runtime Layout Control in LPAI](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#runtime-layout-control-in-lpai)

    - [Purpose](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#purpose)
    - [Why It Matters](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#why-it-matters)
    - [Recommended Usage](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#recommended-usage)
    - [Limitations](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#limitations)
    - [Summary](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#summary)
- [Full JSON Scheme](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#full-json-scheme)

    - [Full JSON Example](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#full-json-example)
- [Best Practices](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#best-practices)

### [Overview](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#contents)

This document outlines the structure and usage of LPAI backend configuration files employed by QNN tools such as `qnn-net-run` and `qnn-context-binary-generator`.
These JSON-formatted files enable fine-grained control over model preparation, runtime behavior, debugging, profiling, and internal backend features.

There are two primary JSON configuration files:

1. **Backend Extension Configuration File**
Specifies the path to the LPAI backend extension shared library and the path to the LPAI backend configuration file.

    Example usage:
`--config_file <path_to_backend_extension_JSON>`

    Example format:

{
            "backend_extensions" : {
                "shared_library_path" : "path_to_Lpai_extension_shared_library",
                "config_file_path" : "path_to_Lpai_extension_config_file"
            }
        }
        Copy to clipboard
2. **LPAI Backend Configuration File**
Defines all configurable parameters for model generation and execution. This file is parsed by the LPAI backend extension library.

### [Configuration Schema](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#contents)

The configuration is organized into the following sections:

- `lpai_backend`: Global backend settings.
- `lpai_graph`  : Graph generation and execution parameters.
- `lpai_profile`: Profiling options (optional).
- `lpai_debug`  : Debug options (optional).

Each section and its parameters are described below.

#### [lpai_backend](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#contents)

- `target_env` (string):

    Target environment for model execution.

    **Options**: `arm`, `adsp`, `x86`
**Default**: `adsp`
- `enable_hw_ver` (string):

    Hardware version of target refer to Supported Snapdragon Devices.

    **Options**: `v5`, `v5_1`, `v6`
**Default**: `v6`

#### [lpai_graph](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#contents)

- `execute`

> 
> 
> Used by `qnn-net-run` during runtime execution.
> 
>     - `fps` (integer): [Target frames per second.](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend_setup_configuration.html#qnn-lpai-configuration-parameters) Default: `1`
>     - `ftrt_ratio` (integer): [Frame-to-real-time ratio.](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend_setup_configuration.html#qnn-lpai-configuration-parameters) Default: `10`
>     - `client_type` (string): [Type of workload.](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend_setup_configuration.html#qnn-lpai-real-time) Options: `real_time`, `non_real_time`. Default: `real_time`
>     - `affinity` (string): [Core affinity policy.](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend_setup_configuration.html#qnn-lpai-core-selection) Options: `soft`, `hard`. Default: `soft`
>     - `core_selection` (integer): [Specific core number.](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend_setup_configuration.html#qnn-lpai-core-selection) Default: `0`

#### [lpai_profile (Optional)](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#contents)

- `level` (string): Profiling level: `basic`, `detailed`. Default: `basic` [Lpai Profiling](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend_profiler.html#qnn-lpai-profiling)

#### [lpai_debug (Optional)](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#contents)

- `force_nhwc` (bool): [Enforce NHWC tensor layout.](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend_setup_configuration.html#qnn-lpai-debug-force-nhwc) Default: `false`

### [QNN LPAI Backend Configuration Parameters](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#contents)

#### [Fps and ftrt_ratio information](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#contents)

These parameters define how a client configures its processing behavior for eNPU hardware.

- - <cite>fps</cite> (Frames Per Second)
    - - Specifies how frequently inference must be completed.
    - For example, <cite>fps = 10</cite> means the system must process one frame every 100 milliseconds (i.e., 1000 ms / 10).
    - This sets the overall time budget for each frame, including pre-processing, inference, and post-processing.
- - <cite>ftrt_ratio</cite> (Factor to Real-Time Ratio)
    - - Determines the hardware configuration to meet the latency requirement for inference.
    - If pre- and post-processing take up most of the frame time (e.g., 80 ms out of 100 ms), only 20 ms remain for inference.
    - To ensure inference completes within this reduced time window, the eNPU must be boosted.
    - Setting <cite>ftrt_ratio = 50</cite> applies a **multiplication factor of 5.0** to the base clock frequency, helping the eNPU meet the tighter latency constraint.
- - Default Values
    - - <cite>fps = 1</cite> (1 frame per second, allowing 1000 ms per frame)
    - <cite>ftrt_ratio = 10</cite> (moderate clock scaling factor)

These defaults imply a relaxed processing schedule and a balanced performance-power tradeoff.

#### [Realtime vs Non-Realtime client](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#contents)

- Real-time: Indicates that the model is intended for real-time use cases, where a specific performance threshold must be met.
If the required performance cannot be achieved, the finalize function will return an error.
- Non-real-time: Refers to models without strict performance requirements.
In these cases, LPAI will make a best-effort attempt to accommodate the workload, and finalize will not fail due to performance limitations.

#### [Core Selection & Affinity](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#contents)

Clients can configure **core selection** and **affinity settings** for the eAI to control how their model’s offloaded operations (Ops) are assigned to processing cores.

If no settings are provided:

- **Core Selection** defaults to `0x00` (no specific preference — any available core may be selected).
- **Affinity** defaults to **soft affinity**.

##### Core Selection

- `coreSelection` is a **bitmask** that specifies which core(s) are eligible for selection.
- Each bit represents a core:

    - `0x01` → selects **core 0**
    - `0x02` → selects **core 1**
    - `0x00` → no specific preference; any available core may be selected

Important

- Mixed core selection (e.g., `0x03` to select both core 0 and core 1) is **not yet supported**.

##### Platform-Specific Guidance

For platforms with **only one processing core**, users should configure:

- `coreSelection = 0x00` (no specific preference), or
- `coreSelection = 0x01` (explicitly select core 0)

This ensures compatibility and avoids undefined behavior due to unsupported multi-core selection.

Important

- The API does **not expose core characteristics** (e.g., whether a core is “big” or “small”).
- Users should consult **platform documentation** to determine core capabilities and make informed decisions about core selection and affinity strategy.

##### Affinity Strategy

- **Hard Affinity**: Forces Ops to run only on the selected core.
- **Soft Affinity**: Prefers the selected core but allows fallback to another if the preferred is busy.

##### Guidance Based on Core Behavior

| Scenario | Recommended coreSelection | Affinity Type | Rationale |
| --- | --- | --- | --- |
| Heavy compute workloads<br>(e.g., large convNets) | `0x02` (Core 1) | Hard or Soft | Core 1 is typically a big core, offering better performance |
| Audio use cases | `0x01` (Core 0) | Soft | Core 0 (small core) is sufficient and more power-efficient |
| Camera use cases | `0x02` (Core 1) | Soft | Core 1 provides faster inference for image processing |
| Shared workloads<br>(audio + camera) | `0x00` (Any) | Soft | Allows dynamic load balancing across cores |
| Power-sensitive applications | `0x01` (Core 0) | Soft | Core 0 consumes less power |
| Performance-critical apps | `0x02` (Core 1) | Hard | Ensures consistent execution on the high-performance core |

##### System-Level Considerations

- Core affinity should be tuned based on:

    - System concurrency
    - Workload characteristics
    - KPI targets
    - Power budget
    - Profiling results
- **Core shutdown is not required**: Idle cores are automatically **power collapsed**, ensuring efficient power management.

### [Runtime Layout Control in LPAI](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#contents)

#### [Purpose](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#contents)

The `force_nhwc` option is a runtime configuration setting used in Qualcomm’s LPAI (Low Power AI) backend to enforce NHWC tensor layout during model execution.
Its primary role is to help avoid automatic layout transformations—specifically `TRANSPOSE` operations—around convolutional layers, which can negatively impact performance and profiling clarity.

#### [Why It Matters](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#contents)

When executing models on the eNPU, layout transformations often appear around operations like `Conv2D`, especially at graph boundaries.
These transformations are inserted to reconcile differences between the model’s tensor layout (e.g., NHWC) and the eNPU’s internal hardware-native layout, which is typically blocked or tiled.

Even if a model is converted with NHWC input/output layouts and no output layout is explicitly forced, the runtime may still insert `TRANSPOSE` operations unless `force_nhwc` is enabled.
These transformations can dominate execution time on the DSP and obscure the performance of the actual accelerated operation.

#### [Recommended Usage](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#contents)

To minimize or eliminate layout transformations at graph boundaries:

- Set input and output tensor layouts to NHWC during model conversion.
- Enable `force_nhwc` in the runtime configuration. This instructs the runtime to preserve NHWC layout and avoid inserting layout transforms.
- Avoid forcing output layout during conversion, which can trigger post-processing transforms.

#### [Limitations](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#contents)

- If `force_nhwc` is not enabled, layout transforms will likely appear even if the graph is NHWC.
- For single operations at graph boundaries, layout transforms may still occur due to the eNPU’s internal layout requirements.
- To fully avoid layout transforms, it is often necessary to chain multiple eNPU-compatible operations, allowing the internal layout to be reused across ops without conversion.

#### [Summary](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#contents)

`force_nhwc` is a critical setting for developers aiming to optimize LPAI model execution and profiling.
It ensures that NHWC layouts are respected at runtime, reducing overhead and improving clarity in performance analysis.
However, due to hardware constraints, some layout transforms may still be unavoidable unless multiple operations are chained together.

### [Full JSON Scheme](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#contents)

Below is a complete scheme of the LPAI backend configuration file with all supported parameters:

{
       "lpai_backend": {
    
          // Selection of targets [options: arm/adsp/x86] [default: adsp] (Simulator or target)
          // Used by qnn-context-binary-generator during offline generation
          "target_env": "adsp",
    
          // Corresponds to the LPAI hardware version [options: v5/v5_1/v6] [default: v6]
          // Used by qnn-context-binary-generator during offline generation
          "enable_hw_ver": "v6"
       },
       "lpai_graph": {
          "execute": {
    
             // Specify the fps rate number, used for clock voting [options: number] [default: 1]
             // Used by qnn-net-run during execution
             "fps": {"type": "integer"},
    
             // Specify the ftrt_ratio number [options: number] [default: 10]
             // Used by qnn-net-run during execution
             "ftrt_ratio": {"type": "integer"},
    
             // Definition of client type [options: real_time/non_real_time] [default: real_time]
             // Used by qnn-net-run during execution
             "client_type": {"type": "string"},
    
             // Definition of affinity type [options: soft/hard] [default: soft]
             // Used by qnn-net-run during execution
             "affinity": {"type": "string"},
    
             // Specify the core number [options: number] [default: 0]
             // Used by qnn-net-run during execution
             "core_selection": {"type": "integer"}
          }
       }
    }
    Copy to clipboard

#### [Full JSON Example](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#contents)

Below is a complete example of the LPAI backend configuration file with all supported parameters:

{
       "lpai_backend": {
          "target_env": "adsp",
          "enable_hw_ver": "v6"
       },
       "lpai_graph": {
          "execute": {
             "fps": 1,
             "ftrt_ratio": 10,
             "client_type": "real_time",
             "affinity": "soft",
          }
       }
    }
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### [Best Practices](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#contents)

- **Minimal Changes**: Use default values unless specific tuning is required.
- **Validation**: Ensure all values conform to expected types and allowed options.
- **Version Compatibility**: Refer to the Supported Snapdragon Devices for supported LPAI versions.

## QNN LPAI Model Generation

Model generation relies on three existing QNN (not QAIRT) tools:

> 
> 
> 1. QNN Model converter Tool responsible for model conversion and quantization to QNN format refer to QNN Converters.
> 2. QNN Model Lib Generator responsible for model compilation to shared library format qnn-model-lib-generator.
> 3. QNN Context Binary Generator is used for offline model compilation qnn-context-binary-generator.
> 
> 
> 
> 
> > 
> > 
> > With the configuration file prepared, you can now generate the model offline. This step compiles the neural network into a format optimized for the target LPAI hardware.
> > 
> > 
> > Important
> > 
> > 
> > The LPAI backend **requires quantized QNN models**. Unquantized QNN models are not supported.
> > 
> > 
> > For supported operations and quantization requirements, refer to the Supported Operations.

[Offline LPAI Model Generation](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend_model_generation.html#qnn-lpai-offline-model-generation) illustrates the LPAI offline model generation.

**Offline LPAI Model Generation**

![Offline LPAI Model Generation](data:image/png;base64,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)

## Quantization Method Comparison

| Method | Command Line | Purpose | Key Options & Descriptions |
| --- | --- | --- | --- |
| CPU-Based Quantization | `qnn-onnx-converter --target_backend LPAI --input_network model.onnx --input_list <path>` | Generate activation distribution and quantize tensors using CPU. | <ul class="simple"><br><li><p><code class="docutils literal notranslate"><span class="pre">--use_per_channel_quantization</span></code>: Enables per-channel quantization for conv-based weights.</p></li><br><li><p><code class="docutils literal notranslate"><span class="pre">--use_per_row_quantization</span></code>: Enables per-row quantization for Matmul/FullyConnected weights.</p></li><br><li><p><code class="docutils literal notranslate"><span class="pre">--act_quantizer_schema</span> <span class="pre">symmetric</span></code>: Sets symmetric quantization for all activations (default: asymmetric).</p></li><br><li><p><code class="docutils literal notranslate"><span class="pre">--param_quantizer_schema</span> <span class="pre">symmetric</span></code>: Sets symmetric quantization for all parameters (default: asymmetric).</p></li><br><li><p><code class="docutils literal notranslate"><span class="pre">--param_quantizer_calibration</span> <span class="pre">min-max</span></code>: Calibration method for parameters (default: min-max).</p></li><br><li><p><code class="docutils literal notranslate"><span class="pre">--act_quantizer_calibration</span> <span class="pre">min-max</span></code>: Calibration method for activations (default: min-max).</p></li><br><li><p><code class="docutils literal notranslate"><span class="pre">--act_bitwidth</span> <span class="pre">8</span></code>: Bitwidth for activations (default: 8-bit).</p></li><br><li><p><code class="docutils literal notranslate"><span class="pre">--weights_bitwidth</span> <span class="pre">8</span></code>: Bitwidth for weights (default: 8-bit).</p></li><br><li><p><code class="docutils literal notranslate"><span class="pre">--bias_bitwidth</span> <span class="pre">32</span></code>: Bitwidth for bias tensors (default: 8-bit).</p></li><br><li><p><code class="docutils literal notranslate"><span class="pre">--use_dynamic_16_bit_weights</span></code>: Keeps matmul/FC weights in 16-bit if specified.</p></li><br><li><p><code class="docutils literal notranslate"><span class="pre">--disable_batchnorm_folding</span></code>: (Optimization) Disables batch normalization folding.</p></li><br><li><p><code class="docutils literal notranslate"><span class="pre">--disable_relu_squashing</span></code>: (Optimization) Disables relu squashing.</p></li><br><li><p><code class="docutils literal notranslate"><span class="pre">--preserve_io</span> <span class="pre">layout</span></code>: Ensured graph input/output layout is the same with the original model.</p></li><br></ul> |
| JSON-Based Quantization (e.g., AIMET) | `qnn-onnx-converter --target_backend LPAI --input_network model.onnx --quantization_overrides --float_fallback` | Use external tool (e.g., AIMET) to define quantization via JSON overrides. | <ul class="simple"><br><li><p><code class="docutils literal notranslate"><span class="pre">--act_quantizer_schema</span></code>: Sets quantization schema for activations (default: asymmetric).</p></li><br><li><p><code class="docutils literal notranslate"><span class="pre">--param_quantizer_schema</span></code>: Sets quantization schema for parameters (default: asymmetric).</p></li><br><li><p><code class="docutils literal notranslate"><span class="pre">--bias_bitwidth</span> <span class="pre">32</span></code>: Bitwidth for bias tensors (default: 8-bit).</p></li><br><li><p><code class="docutils literal notranslate"><span class="pre">--keep_weights_quantized</span></code>: Retains weight quantization info even if output is float.</p></li><br><li><p><code class="docutils literal notranslate"><span class="pre">--disable_batchnorm_folding</span></code>: (Optimization) Disables batch normalization folding.</p></li><br><li><p><code class="docutils literal notranslate"><span class="pre">--disable_relu_squashing</span></code>: (Optimization) Disables relu squashing.</p></li><br><li><p><code class="docutils literal notranslate"><span class="pre">--preserve_io</span> <span class="pre">layout</span></code>: Ensured graph input/output layout is the same with the original model.</p></li><br></ul> |

Note

`--use_dynamic_16_bit_weights` is **enabled by default** when using `--target_backend LPAI`.

## Recommendations

| Scenario | Recommended Workflow | Rationale |
| --- | --- | --- |
| Quick prototyping or baseline quantization | CPU-Based Quantization | Fast setup with default options; no need for external tools. |
| Fine-grained control over quantization behavior | JSON-Based Quantization | Allows detailed overrides via JSON (e.g., AIMET), ideal for achieving better quantization accuracy with advanced quantization algorithms. |
| Working with pre-calibrated models or external calibration tools | JSON-Based Quantization | Integrates well with external calibration data and overrides. |

## Quantization Tips

- Use `--use_per_channel_quantization` for convolution layers to improve accuracy.
- Use `--use_per_row_quantization` for Matmul/FC layers to reduce quantization error.
- Prefer symmetric quantization for hardware-friendly deployment.
- Use `--keep_weights_quantized` and `--float_fallback` combination to make sure quantization behavior specified in the quantization overrides is retained.

### Preparing LPAI Param Config File

Prepare Json file with appropriate parameters to generate model for appropriate hardware

EXAMPLE of `lpaiParams.conf` file for v6 hardware:

{
       "lpai_backend": {
          "target_env": "x86",
          "enable_hw_ver": "v6"
       }
    }
    Copy to clipboard

## Compile LPAI Graph on x86 Linux OS

EXAMPLE of `config.json` file:

{
       "backend_extensions": {
       "shared_library_path": "${QNN_SDK_ROOT}/lib/x86_64-linux-clang/libQnnLpaiNetRunExtensions.so",
       "config_file_path": "./lpaiParams.conf"
       }
    }
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Use context binary generator to generate offline LPAI model.

The qnn-context-binary-generator utility is backend-agnostic, meaning it can only utilize generic QNN APIs.
The backend extension feature allows for the use of backend-specific APIs, such as custom configurations.
More documentation on context binary generator can be found under qnn-context-binary-generator
Please note that the scope of QNN backend extensions is limited to qnn-context-binary-generator and qnn-net-run.

LPAI Backend Extensions serve as an interface to offer custom options to the LPAI Backend.
To enable hardware versions, it is necessary to provide an extension shared library
`libQnnLpaiNetRunExtensions.so` and a configuration file, if required.

To use backend extension-related parameters with qnn-net-run and qnn-context-binary-generator, use the `--config_file` argument and provide the path to the JSON file.

$ cd ${QNN_SDK_ROOT}/examples/QNN/converter/models
    $ export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:${QNN_SDK_ROOT}/lib/x86_64-linux-clang
    $ ${QNN_SDK_ROOT}/bin/x86_64-linux-clang/qnn-context-binary-generator \
            --backend <path_to_x86_library>/libQnnLpai.so \
            --model <qnn_x86_model_name.so> \
            --log_level verbose \
            --binary_file <qnn_model_name.bin> \
            --config_file <path to JSON of backend extensions>
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To configure LPAI JSON configuration refer to [QNN LPAI Backend Configuration Guide](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#qnn-lpai-backend-configuration-guide)

## Compile LPAI Graph on x86 Windows OS

EXAMPLE of `config.json` file:

{
       "backend_extensions": {
          "shared_library_path": "${QNN_SDK_ROOT}/lib/x86_64-windows-msvc/QnnLpaiNetRunExtensions.dll",
          "config_file_path": "./lpaiParams.conf"
       }
    }
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Note

The `lpaiParams.conf` should be created in a manner similar to that used for Linux operating systems.

Use the context binary generator to generate an offline LPAI model.
More documentation on the context binary generator can be found under qnn-context-binary-generator.

**Generate the Context Binary:**

cd ${QNN_SDK_ROOT}/examples/QNN/converter/models
    $env:PATH=$PATH:"${QNN_SDK_ROOT}/lib/x86_64-windows-msvc:${QNN_SDK_ROOT}/bin/x86_64-windows-msvc"
    qnn-context-binary-generator.exe `
        --backend ${QNN_SDK_ROOT}/lib/x86_64-windows-msvc/QnnLpai.dll `
        --model ${QNN_SDK_ROOT}/examples/Models/InceptionV3/model_libs/x86_64-windows-msvc/QnnModel.dll `
        --config_file <config.json> `
        --binary_file qnn_model_8bit_quantized.serialized
    Copy to clipboard

## QNN LPAI Execution

**Transfer model and input files to the target device**

> 
> 
> Set up a dedicated test directory on the target device or x86 host (for simulation). Copy the following into this directory:
> 
> - The compiled model binary
> - Input data files
> - A predefined <cite>input_list</cite> file (for QNN-NET-RUN)
> 
> 
> 
> Ensure that all required QNN and LPAI runtime components are included based on the target platform
> [Execute model using qnn-net-run](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_execution_arm_tutorial.html#qnn-lpai-fastrpc-backend-type) to learn more about necessary components for every configuration.

**Execute the model using qnn-net-run on test platform (simulator or target device)**

> 
> 
> Execute the model on supported platforms (x86, ARM, DSP). For execution instructions, refer to:
> 
> 1. [x86 Lpai backend.](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend_model_execution.html#qnn-lpai-sim-execution) The same backend used during offline model generation. It can also simulate how the model would run in real-time, making it useful for testing and validation purposes.
> 2. [ARM Lpai backend.](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_execution_arm_tutorial.html#qnn-lpai-fastrpc-backend-type) This backend is less efficient due to latency introduced by FastRPC communication, which impacts overall performance.
> 3. [Native DSP Lpai backend.](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_execution_direct_tutorial.html#qnn-lpai-direct-mode-backend-type) This backend type is designed to execute directly on the DSP, to run models on this backend, qnn-net-run should be used in direct mode.

[LPAI Simulation behavior](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend_model_execution.html#qnn-lpai-sim-execution) illustrates the LPAI simulation on x86 Linux/Windows OS and Hexagon.

## QNN LPAI Backend Simulation

The LPAI backend compiled for x86 platform supports both offline model generation and direct execution using a simulator.
This capability allows clients to debug and deploy their models more quickly on an x86 machine without needing to interact directly with the target device.

Refer the offline model generation page to prepare configuration files ahead [Offline LPAI Model Generation](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend_model_generation.html#qnn-lpai-offline-model-generation).

## QNN LPAI Simulation on Linux x86

**LPAI x86 Linux Simulation**

![LPAI x86 Linux Simulation](data:image/png;base64,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)

Note

If full paths are not given to `qnn-net-run`, all libraries must be added to
`LD_LIBRARY_PATH` and be discoverable by the system library loader.

**From Quantized model:**

$ cd ${QNN_SDK_ROOT}/examples/QNN/converter/models
    $ ${QNN_SDK_ROOT}/bin/x86_64-linux-clang/qnn-net-run \
                  --backend ${QNN_SDK_ROOT}/lib/x86_64-linux-clang/libQnnLpai.so \
                  --model ${QNN_SDK_ROOT}/examples/QNN/example_libs/x86_64-linux-clang/libQnnModel.so  \
                  --input_list ${QNN_SDK_ROOT}/examples/QNN/converter/models/input_list_float.txt \
                  --config_file /path/to/config.json
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**From Serialized buffer:**

$ cd ${QNN_SDK_ROOT}/examples/QNN/converter/models
    $ ${QNN_SDK_ROOT}/bin/x86_64-linux-clang/qnn-net-run \
                  --backend ${QNN_SDK_ROOT}/lib/x86_64-linux-clang/libQnnLpai.so \
                  --retrieve_context ${QNN_SDK_ROOT}/examples/QNN/converter/models/qnn_model_8bit_quantized.serialized.bin \
                  --input_list ${QNN_SDK_ROOT}/examples/QNN/converter/models/input_list_float.txt \
                  --config_file /path/to/config.json
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Tip

Add the necessary libraries to your `LD_LIBRARY_PATH`:

export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:${QNN_SDK_ROOT}/lib/x86_64-linux-clang
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## QNN LPAI Simulation on Windows x86

**LPAI x86 Windows Simulation**

![LPAI x86 Windows Simulation](data:image/png;base64,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)

Follow these steps to run the LPAI Simulation Backend on a Windows x86 system:

Note

If full paths are not given to `qnn-net-run.exe`, all libraries must be added to
`PATH` and be discoverable by the system library loader.

**From Quantized model:**

$ cd ${QNN_SDK_ROOT}/examples/QNN/converter/models
    $ ${QNN_SDK_ROOT}/bin/x86_64-windows-msvc/qnn-net-run.exe \
                  --backend ${QNN_SDK_ROOT}/lib/x86_64-windows-msvc/QnnLpai.dll \
                  --model ${QNN_SDK_ROOT}/examples/QNN/example_libs/x86_64-windows-msvc/QnnModel.dll \
                  --input_list ${QNN_SDK_ROOT}/examples/QNN/converter/models/input_list_float.txt \
                  --config_file /path/to/config.json
    Copy to clipboard

**From Serialized buffer:**

$ cd ${QNN_SDK_ROOT}/examples/QNN/converter/models
    $ ${QNN_SDK_ROOT}/bin/x86_64-windows-msvc/qnn-net-run.exe \
                  --backend ${QNN_SDK_ROOT}/lib/x86_64-windows-msvc/QnnLpai.dll \
                  --retrieve_context ${QNN_SDK_ROOT}/examples/QNN/converter/models/qnn_model_8bit_quantized.serialized.bin \
                  --input_list ${QNN_SDK_ROOT}/examples/QNN/converter/models/input_list_float.txt \
                  --config_file /path/to/config.json
    Copy to clipboard

Important

Ensure that the `QNN_SDK_ROOT` environment variable is set correctly:

set QNN_SDK_ROOT=C:\path\to\qnn_sdk
    Copy to clipboard

Tip

Add the necessary libraries to your `PATH`:

set PATH=%PATH%;%QNN_SDK_ROOT%\lib\x86_64-windows-msvc
    Copy to clipboard

Outputs from the run will be located at the default ./output directory.

\_hexagon\_sim\_execution:

## Hexagon Simulator Execution Guide (Advanced – LPAI v6)

This section provides a **detailed, customer‑focused guide** for running, testing,
debugging, and validating the **hexagon\_sim application** using the
`run_hexagon_sim.sh` script.

The guide reflects the **exact behavior implemented in the script**, including
implicit configuration generation and simulator setup that users do not need
to manage manually. Its goal is to help customers:

- Understand what the script configures automatically
- Provide correct inputs and parameters
- Validate functional correctness
- Debug failures systematically and efficiently

Execution targets a **Hexagon v79 simulated environment** with
**LPAI v6 ENPU co‑simulation enabled**.

## Audience

This guide is intended for:

- Customers integrating QNN LPAI models
- Validation and verification engineers
- Developers debugging backend, model, or simulator behavior

A basic understanding of QNN concepts (context, backend, system library)
is assumed. No prior knowledge of Hexagon simulator internals is required.

## What <cite>run_hexagon_sim.sh</cite> Does

The `run_hexagon_sim.sh` script acts as a **controlled execution harness**
that prepares and launches a Hexagon simulation session with ENPU enabled.

Internally, the script performs the following actions automatically:

1. Determines execution paths and working directory
2. Locates the Hexagon simulator from the SDK
3. Generates required RTOS and ENPU configuration files
4. Determines and creates the output directory
5. Launches `hexagon-sim` with the correct parameters
6. Executes a QNN sample application inside the simulated environment

From a user’s perspective, the complexity is intentionally reduced to:

- One application shared library
- Standard QNN application arguments

All application arguments are forwarded **unchanged**, ensuring behavior is
consistent with execution on real hardware.

## Command Line Interface

### Basic Usage

./run_hexagon_sim.sh {app_so} {app_args}
    Copy to clipboard

Where:

- `{app_so}`
Path to the **QNN sample application shared library** built with the
`hexagon_sim` option enabled.
- `{app_args}`
One or more **QNN application arguments** passed directly to the sample
application. The script does not interpret or modify these arguments.

If no arguments are provided, the script prints usage information and exits.

## Environment Variables

The script supports externally defined environment variables, falling back
to defaults if they are not set.

### HEXAGON\_SDK\_PATH

Path to the Hexagon SDK installation. This variable is required to locate
`hexagon-sim` and simulator runtime components.

If not explicitly set, the script uses a built‑in default path.

export HEXAGON_SDK_PATH=/path/to/hexagon-sdk
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Failure modes:
- `hexagon-sim not found` → incorrect `HEXAGON_SDK_PATH`

### HEXAGON\_SIM\_PATH

Path to the directory containing Hexagon simulator artifacts and ENPU
components.

If not set, the script defaults to the **directory containing``run\_hexagon\_sim.sh``**.

export HEXAGON_SIM_PATH=/path/to/hexagon_sim_artifacts
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## Generated Runtime Configuration

The script automatically generates the following configuration files
in the current working directory on each run.

### QURT RTOS Configuration (`osam.cfg`)

`osam.cfg` is generated to configure a minimal QURT RTOS environment
required by the simulator.

The file references the QURT model distributed with the Hexagon SDK
and is regenerated on each invocation.

### ENPU Co‑Simulation Configuration (`enpu_cosim.cfg`)

`enpu_cosim.cfg` configures ENPU (LPMLA) hardware co‑simulation and includes:

- Two LPMLA hardware instances
- L2VIC configuration
- Qtimer configuration required by QURT

All addresses, interrupts, and hardware parameters are **fixed in the script**
and represent a known‑good ENPU v6 simulation setup.

Users should not modify this file unless explicitly instructed.

## Output Directory Handling

The script determines the output directory as follows:

- If `--output_dir <dir>` is present in the application arguments,
that directory is used
- Otherwise, the default directory `./output` is selected

The script **creates the output directory automatically** before launching
the simulator.

Execution fails if:
- The directory cannot be created
- The path exists but is not a directory

Note

Using a unique output directory per run is strongly recommended
for debugging and result comparison.

## Example: Minimal Working Invocation

The following invocation represents a **baseline sanity test**:

./run_hexagon_sim.sh \
      ../lib/unsigned/libqnn-sample-app-lpai.so \
      --retrieve_context ./model.bin \
      --backend ../lib/unsigned/libQnnLpai.so \
      --systemlib ../../hexagon_v79/lib/unsigned/libQnnSystem.so
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Successful execution confirms:
- Simulator startup
- ENPU configuration validity
- Backend and system library compatibility

## Commonly Used Application Parameters

### `--retrieve_context <model.bin>`

Specifies the serialized QNN context generated by the LPAI backend.

The context encodes:
- Graph structure
- Quantization parameters
- Backend optimizations

Context load failures typically indicate version or backend mismatch.

### `--backend <libQnnLpai.so>`

Specifies the QNN LPAI v6 backend implementation.

The library must:
- Target Hexagon v79
- Match the backend used during model generation

### `--systemlib <libQnnSystem.so>`

Specifies the QNN system library providing low‑level runtime services.

This library must match:
- Hexagon v79
- The SDK used by the simulator

### `--input_list <input_list.txt>`

Specifies a text file listing input data files (one per line).

Each input file must:
- Match expected tensor shapes
- Use correct data types
- Be **pre‑quantized**

### `--output_dir <directory>`

Specifies where output files are written. Automatically created by the script.

## Input and Output Quantization Requirements

The QNN sample application executed by `run_hexagon_sim.sh` **does not performquantization or dequantization**.

All inputs and outputs are treated as **already‑quantized tensors**.

This implies:

- Input data **must be pre‑quantized** using the model’s quantization parameters
- Output data is produced in **quantized form**
- Any dequantization or post‑processing must be handled externally

Note

Providing floating‑point input data may not produce a runtime error,
but will result in incorrect or meaningless outputs.

## Execution Flow

At runtime, the following sequence occurs:

1. Argument validation
2. Generation of `osam.cfg`
3. Generation of `enpu_cosim.cfg`
4. Output directory creation
5. Invocation of `hexagon-sim` with:
- Hexagon v79 target
- ENPU v6 co‑simulation
- RTOS and cosim configuration
6. Execution of the QNN sample application

Indicators of successful execution include:
- No startup or backend errors
- Context loaded successfully
- Output artifacts generated as expected

## Debugging and Validation Guidance

Start with simple inputs and a single inference run. Validate:

- Output files are created
- Output sizes and counts are correct
- Quantized outputs dequantize correctly against a reference

Inspect `osam.cfg` and `enpu_cosim.cfg` when:
- Escalating simulator issues
- Comparing runs
- Reproducing failures

## Common Failure Scenarios

- **hexagon-sim not found**
→ `HEXAGON_SDK_PATH` incorrect
- **Shared library load failure**
→ Architecture or ABI mismatch
- **Context creation failure**
→ Incompatible backend or model
- **Execution succeeds but outputs are incorrect**
→ Inputs not quantized or outputs misinterpreted

## Key Limitations

- ENPU configuration is fixed for **LPAI v6**
- Hexagon simulator target is fixed to **v79**
- Intended for **functional validation and debugging**, not
cycle‑accurate performance measurement

[LPAI ARM Backend Type](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_execution_arm_tutorial.html#qnn-lpai-fastrpc-backend-type) illustrates the LPAI ARM backend type execution.

## QNN LPAI ARM Backend Type

**LPAI ARM Backend Type Execution**

![LPAI ARM Backend Type Execution](data:image/png;base64,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Running the LPAI Backend on an Android device via an ARM target is supported exclusively for offline-prepared graphs.
This tutorial outlines the process of preparing the graph on an x86 host and subsequently transferring the serialized
context binary to the device’s LPAI Backend for execution.

To ensure compatibility with a specific target platform, it is essential to use libraries compiled for that particular target.
Examples are provided below. The QNN\_TARGET\_ARCH variable can be utilized to specify the appropriate library for the target.

### Setting Environment Variables on x86 Linux

# Example for Android targets (Not all targets are supported for Android)
    $ export QNN_TARGET_ARCH=aarch64-android
    
    # Example for LE Linux targets (If applicable)
    $ export QNN_TARGET_ARCH=aarch64-oe-linux-gcc<your version>
    
    # Example for QNX targets (If applicable)
    $ export QNN_TARGET_ARCH=aarch64-qnx800
    
    # For LPAI v6 HW version
    $ export HW_VER=v6
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### Prepare config.json file

{
       "backend_extensions": {
       "shared_library_path": "/data/local/tmp/LPAI/libQnnLpaiNetRunExtensions.so",
       "config_file_path": "./lpaiParams.conf"
       }
    }
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Note

To run the LPAI backend on an Android device, the following requirements must be fulfilled:

1. `${QNN_SDK_ROOT}/lib/lpai-${HW_VER}/unsigned/libQnnLpaiSkel.so` has to be signed by client
2. `qnn-net-run` to be executed with root permissions

### Create test directory on the device

$ adb shell mkdir -p /data/local/tmp/LPAI/adsp
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### Push the quantized model to the device

$ adb push ./output/qnn_model_8bit_quantized.serialized.bin /data/local/tmp/LPAI
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### Push the LPAI related libraries to the device

$ adb push ${QNN_SDK_ROOT}/lib/${QNN_TARGET_ARCH}/libQnnLpai.so /data/local/tmp/LPAI
    $ adb push ${QNN_SDK_ROOT}/lib/${QNN_TARGET_ARCH}/libQnnLpaiStub.so /data/local/tmp/LPAI
    $ adb push ${QNN_SDK_ROOT}/lib/${QNN_TARGET_ARCH}/libQnnLpaiNetRunExtensions.so /data/local/tmp/LPAI
    $ # Additionally, the LPAI requires Hexagon specific libraries
    $ adb push ${QNN_SDK_ROOT}/lib/lpai-${HW_VER}/unsigned/libQnnLpaiSkel.so /data/local/tmp/LPAI/adsp
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### Push the input data and input lists to the device

$ adb push ${QNN_SDK_ROOT}/examples/QNN/converter/models/input_data_float /data/local/tmp/LPAI
    $ adb push ${QNN_SDK_ROOT}/examples/QNN/converter/models/input_list_float.txt /data/local/tmp/LPAI
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### Push the qnn-net-run tool

$ adb push ${QNN_SDK_ROOT}/bin/aarch64-android/qnn-net-run /data/local/tmp/LPAI
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### Set up the environment on the device

$ adb shell
    $ cd /data/local/tmp/LPAI
    $ export LD_LIBRARY_PATH=/data/local/tmp/LPAI:/data/local/tmp/LPAI/adsp
    $ export ADSP_LIBRARY_PATH="/data/local/tmp/LPAI/adsp"
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### Execute the LPAI model using qnn-net-run

$ ./qnn-net-run --backend ./libQnnLpai.so --device_options device_id:0 --retrieve_context ./qnn_model_8bit_quantized.serialized.bin --input_list ./input_list_float.txt
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[LPAI Native DSP Backend type](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_execution_direct_tutorial.html#qnn-lpai-direct-mode-backend-type) illustrates the LPAI Native DSP Backend type execution.

## QNN LPAI Native aDSP Backend Type

**LPAI Native DSP Backend Type Execution**

![LPAI Native DSP Backend Type Execution](data:image/png;base64,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)

### Overview

The **QNN LPAI Native aDSP Backend Type** is designed to enable efficient execution of the LPAI backend by providing direct access to the DSP (Digital Signal Processor) hardware.
This approach eliminates the overhead associated with data and control transfer via the IPC (Inter-Process Communication) mechanism, resulting in significantly reduced latency and improved runtime performance.
Applications that can access input sources such as audio, camera, or sensors directly on the aDSP can run independently of the main operating system (Linux/Android).
To use the native aDSP path, these applications must be integrated into either the audio or sensor Process domain (PD).

Execution on a physical device using the native aDSP target is supported **exclusively** for **offline-prepared graphs**. This mode does **not** support dynamic graph compilation or runtime graph generation.

To run on a specific target platform, you must use binaries compiled for that platform. The appropriate library can be selected using the <cite>QNN_TARGET_ARCH</cite> environment variable (see more details below).

### Target Platform Configuration

To deploy the LPAI backend on a specific target, configure the environment using the correct architecture-specific binaries. Set the <cite>QNN_TARGET_ARCH</cite> variable as shown below:

export QNN_TARGET_ARCH=<target_arch>
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Supported target architectures include:

- <cite>aarch64-android</cite> for Android-based ARM64 platforms
- <cite>aarch64-oe-linux-gcc&lt;your version&gt;</cite> for LE Linux-based ARM64 platforms
- <cite>aarch64-qnx800</cite> for QNX-based ARM64 platforms
- <cite>hexagon-v&lt;version&gt;</cite> for Qualcomm Hexagon DSP platforms

Important

**Not all target architectures are supported for Android.**
Some platforms are **lack HLOS (High-Level Operating System) support entirely**. In such cases, HLOS deployment instructions do not apply.
Ensure that your target platform supports the necessary runtime environment for LPAI execution. Refer to the [Available Backend Libraries table](https://docs.qualcomm.com/doc/80-63442-10/topic/backend.html#qnn-sdk-backends-table) for platform-specific compatibility and deployment guidance.

### Setting Environment Variables on HLOS Android/Linux

To configure your development or deployment environment on an x86 Linux host, set the following environment variables:

# Example for Android targets (Not all targets are supported for Android)
    $ export QNN_TARGET_ARCH=aarch64-android
    
    # Example for LE Linux targets (If applicable)
    $ export QNN_TARGET_ARCH=aarch64-oe-linux-gcc<your version>
    
    # Example for QNX targets (If applicable)
    $ export QNN_TARGET_ARCH=aarch64-qnx800
    
    # For Hexagon versrion
    $ export HEX_VER=81
    $ export HEX_ARCH=hexagon-v${HEX_VER}
    
    # For LPAI v6 HW version
    $ export HW_VER=v6
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Note

To execute the LPAI backend on an Android device, the following conditions must be met:

1. The following Lpai artifacts in `${QNN_SDK_ROOT}/lib/lpai-${HW_VER}/unsigned` must be signed by the client:

    - `libQnnLpai.so`
    - `libQnnLpaiNetRunExtensions.so`
2. The following qnn-net-run artifacts in `${QNN_SDK_ROOT}/lib/${HEX_ARCH}/unsigned` must be signed by the client:

    - `libQnnHexagonSkel_dspApp.so`
    - `libQnnNetRunDirectV${HEX_VER}Skel.so`
3. `qnn-net-run` must be executed with root permissions.

Prepare config.json file for direct-mode, where `is_persistent_binary` is required for direct-mode:

{
       "backend_extensions": {
          "shared_library_path": "/data/local/tmp/LPAI/adsp/libQnnLpaiNetRunExtensions.so",
          "config_file_path": "./lpaiParams.conf"
       },
       "context_configs": {
          // This parameter should be set for native aDSP LPAI backend
          "is_persistent_binary": true
       }
    }
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### Create test directory on the device

$ adb shell mkdir -p /data/local/tmp/LPAI/adsp
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### Push the offline LPAI generated model to the device

$ adb push ./output/qnn_model_8bit_quantized.serialized.bin /data/local/tmp/LPAI
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### Push the Lpai libraries to the device

$ adb push ${QNN_SDK_ROOT}/lib/lpai-${HW_VER}/unsigned/libQnnLpai.so /data/local/tmp/LPAI/adsp
    $ adb push ${QNN_SDK_ROOT}/lib/lpai-${HW_VER}/unsigned/libQnnLpaiNetRunExtensions.so /data/local/tmp/LPAI/adsp
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### Push the qnn-net-run libraries to the device

$ adb push ${QNN_SDK_ROOT}/lib/${HEX_ARCH}/unsigned/libQnnHexagonSkel_dspApp.so /data/local/tmp/LPAI/adsp
    $ adb push ${QNN_SDK_ROOT}/lib/${HEX_ARCH}/unsigned/libQnnNetRunDirectV${HEX_VER}Skel.so /data/local/tmp/LPAI/adsp
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### Push the input data and input lists to the device

$ adb push ${QNN_SDK_ROOT}/examples/QNN/converter/models/input_data_float /data/local/tmp/LPAI
    $ adb push ${QNN_SDK_ROOT}/examples/QNN/converter/models/input_list_float.txt /data/local/tmp/LPAI
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### Push the qnn-net-run tool and its dependent libraries

$ adb push ${QNN_SDK_ROOT}/bin/${QNN_TARGET_ARCH}/qnn-net-run /data/local/tmp/LPAI
    $ adb push ${QNN_SDK_ROOT}/lib/${QNN_TARGET_ARCH}/libQnnNetRunDirectV${HEX_VER}Stub.so /data/local/tmp/LPAI
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### Set up the environment on the device

$ adb shell
    $ cd /data/local/tmp/LPAI
    $ export LD_LIBRARY_PATH=/data/local/tmp/LPAI:/data/local/tmp/LPAI/adsp
    $ export ADSP_LIBRARY_PATH="/data/local/tmp/LPAI/adsp"
    $ export HW_VER=v6
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### Execute the LPAI model using qnn-net-run

$ ./qnn-net-run --backend asdp/libQnnLpai.so --direct_mode --retrieve_context ./qnn_model_8bit_quantized.serialized.bin --input_list ./input_list_float.txt --config_file <config.json>
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[LPAI Native DSP Backend type for WoS](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend_model_execution.html#qnn-lpai-direct-mode-backend-type-winarm) illustrates the LPAI Native DSP Backend Windows on ARM type execution.

## QNN LPAI Native aDSP Backend Type for Windows on Arm

**LPAI Native DSP Backend Type Execution on WoS**

![LPAI Native DSP Backend Type Execution on WoS](data:image/png;base64,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)

### Overview

The **QNN LPAI Native aDSP Backend Type on WoS (Windows on Snapdragon)** is designed to enable efficient execution of the LPAI backend by providing direct access to the DSP (Digital Signal Processor) hardware.
This approach eliminates the overhead associated with data and control transfer via the IPC (Inter-Process Communication) mechanism, resulting in significantly reduced latency and improved runtime performance.
Applications that can access input sources such as audio, camera, or sensors directly on the aDSP can run independently of the main operating system.
To use the native aDSP path, these applications must be integrated into either the audio or sensor power domain (PD).

Execution on a physical device using the native aDSP target is supported **exclusively** for **offline-prepared graphs**. This mode does **not** support dynamic graph compilation or runtime graph generation.

To run on a specific target platform, you must use binaries compiled for that platform. The appropriate library can be selected using the <cite>QNN_TARGET_ARCH</cite> environment variable (see more details below).

### Prerequisite

All commands to be executed in windows powershell.
Open C: drive in windows explorer -&gt; Right click anywhere in the window and select “Open in Terminal”

### Target Platform Configuration

To deploy the LPAI backend on a specific target, configure the environment using the correct architecture-specific binaries. Set the <cite>QNN_TARGET_ARCH</cite> variable as shown below:

REM Example for Windows on Arm targets
    $env:QNN_TARGET_ARCH = "aarch64-windows-msvc"
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Supported target architectures include:

- <cite>aarch64-windows-msvc</cite> for Windows on Arm platforms
- <cite>hexagon-v&lt;version&gt;</cite> for Qualcomm Hexagon DSP platforms

Important

Ensure that your target platform supports the necessary runtime environment for LPAI execution. Refer to the [Available Backend Libraries table](https://docs.qualcomm.com/doc/80-63442-10/topic/backend.html#qnn-sdk-backends-table) for platform-specific compatibility and deployment guidance.

### Setting Environment Variables on WoS device

REM Set target architecture
    $env:QNN_TARGET_ARCH = "aarch64-windows-msvc"
    
    REM Set hardware version
    $env:HW_VER="v5"
    
    REM Set Hexagon version
    $env:HEX_VER="81"
    $env:HEX_ARCH="hexagon-v$env:HEX_VER"
    
    REM Set configuration paths where model and configuration files are located
    $env:QNN_MODEL_BIN_PATH = "C:\test"
    $env:QNN_CONFIG_PATH = "C:\test"
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Note

To execute the LPAI backend on a WoS device, the following conditions must be met:

1. The following Lpai artifacts in `$env:QNN_SDK_ROOT\lib\lpai-$env:HW_VER\unsigned` must be signed by the client:

    - `libQnnLpai.so`
    - `libQnnLpaiNetRunExtensions.so`
2. The following qnn-net-run artifacts in `$env:QNN_SDK_ROOT\lib\$env:HEX_ARCH\unsigned` must be signed by the client:

    - `libQnnHexagonSkel_dspApp.so`
    - `libQnnNetRunDirectV$($env:HEX_VER)Skel.so`

Prepare config.json file for direct-mode, where `is_persistent_binary` is required for direct-mode:

{
       "backend_extensions": {
          "shared_library_path": "libQnnLpaiNetRunExtensions.so",
          "config_file_path": "lpaiParams.conf"
       },
       "context_configs": {
          "is_persistent_binary": true
       }
    }
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Prepare lpaiParams.json file as given below:

{
     "lpai_backend": {
          "target_env": "adsp",
          "enable_hw_ver": "v5"
       }
    }
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### Deployment Steps on WoS Device

1. **Create test directory**

mkdir C:\qnn\LPAI
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2. **Copy Configuration Files**

copy $env:QNN_CONFIG_PATH\config.json C:\qnn\LPAI
    copy $env:QNN_CONFIG_PATH\lpaiParams.conf C:\qnn\LPAI
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3. **Copy offline LPAI generated model**

copy $env:QNN_MODEL_BIN_PATH\qnn_model_8bit_quantized.serialized.bin C:\qnn\LPAI
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4. **Copy input data and input lists**

copy $env:QNN_MODEL_BIN_PATH\input_list.txt C:\qnn\LPAI
    Copy-Item -Path "$env:QNN_MODEL_BIN_PATH\inputs" -Destination "C:\qnn\LPAI" -Recurse -Force
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5. **Copy LPAI libraries**

copy $env:QNN_SDK_ROOT\lib\lpai-$env:HW_VER\unsigned\libQnnLpai.so C:\qnn\LPAI
    copy $env:QNN_SDK_ROOT\lib\lpai-$env:HW_VER\unsigned\libQnnLpaiNetRunExtensions.so C:\qnn\LPAI
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6. **Copy qnn-net-run libraries**

copy $env:QNN_SDK_ROOT\lib\$env:HEX_ARCH\unsigned\libQnnHexagonSkel_dspApp.so C:\qnn\LPAI
    copy $env:QNN_SDK_ROOT\lib\$env:HEX_ARCH\unsigned\libQnnNetRunDirectV$($env:HEX_VER)Skel.so C:\qnn\LPAI
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7. **Copy qnn-net-run tool and dependencies**

copy $env:QNN_SDK_ROOT\bin\$env:QNN_TARGET_ARCH\qnn-net-run.exe C:\qnn\LPAI
    copy $env:QNN_SDK_ROOT\lib\$env:QNN_TARGET_ARCH\QnnNetRunDirectV$($env:HEX_VER)Stub.dll C:\qnn\LPAI
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8. **Set up environment and execute model**

cd C:\qnn\LPAI
    $env:PATH="C:\qnn\LPAI;$env:PATH"
    
    qnn-net-run.exe --backend libQnnLpai.so --direct_mode --retrieve_context qnn_model_8bit_quantized.serialized.bin --input_list input_list_float.txt --config_file config.json
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Note

The output folder for WoS is always relative to the driver fastrpc path: C:\Windows\System32\drivers\DriverData\Qualcomm\fastRPC

- For example:
    - qnn-net-run --output test\_output

The output files will be found at:   C:\Windows\System32\drivers\DriverData\Qualcomm\fastRPC\test\_output

- For example:
    - qnn-net-run --output .\another\_test\output

The output files will be found at:     C:\Windows\System32\drivers\DriverData\Qualcomm\fastRPC\another\_test\output

## QNN LPAI Profiling

QNN supports two profiling modes:

- **Per API Profiling**: Captures profiling data for individual QNN API calls. This mode provides fine-grained visibility into the performance of each API invocation.
- **Graph Continuous Profiling**: Captures profiling data across the entire graph execution, offering a holistic view of performance across layers and operations.

Note

The **LPAI backend** currently supports only **Per API Profiling**.

Supported profiling modes for LPAI:

- ✅ Per API Profiling
- ❌ Graph Continuous Profiling

Refer to the following sections for more details:

- [Basic Profiling](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend_profiler.html#qnn-lpai-basic-profiling)
- [Detailed Profiling](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend_profiler.html#qnn-lpai-detailed-profiling)
- [Enable Profiling in qnn-net-run](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#enable-profiling-in-qnn-net-run)
- [Visualize Profile Data with qnn-profile-viewer](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#visualize-profile-data-with-qnn-profile-viewer)

## Profiling Initialization

To enable profiling in the QNN runtime, the following steps must be taken during initialization:

1. **Set Profiling Level**

    Use the <cite>–profiling_level</cite> command-line argument when invoking <cite>qnn-net-run</cite>. Supported values:

    - <cite>basic</cite>: Enables essential profiling events.
    - <cite>detailed</cite>: Enables all available profiling events, including backend-specific metrics.
2. **Ensure Profiling is Enabled in the Backend Configuration**

    The backend configuration file (if applicable) must allow profiling. This may include enabling flags such as:

    - <cite>enableProfiling: true</cite>
    - <cite>profilingOutputPath: &lt;directory&gt;</cite>
3. **Initialize QNN Context with Profiling Support**

    When creating the QNN context (e.g., via <cite>QnnContext_createFromBinary</cite>), ensure that profiling is not disabled by any runtime flags or environment variables.
4. **Execution and Logging**

    During graph execution, profiling data is collected and written to log files in the output directory. These logs are automatically named and versioned.

Note

Profiling introduces some runtime overhead. For performance-sensitive deployments, it is recommended to disable profiling in production environments.

## Basic Profiling

Basic profiling is designed to provide a lightweight overview of performance-critical operations within the QNN runtime and backend.
It is ideal for quick diagnostics, regression testing, and high-level performance monitoring with minimal overhead.

**Scope of Basic Profiling:**

1. **QNN API-Level Events:**

    - Measures the execution time of key QNN API calls:

        - <cite>QnnContext_createFromBinary</cite>: Time taken to deserialize and initialize the context.
        - <cite>QnnGraph_finalize</cite>: Time to finalize the graph before execution.
        - <cite>QnnGraph_execute</cite>: Time spent executing the graph.
        - <cite>QnnContext_free</cite>: Time to release context resources.
2. **Backend-Specific Events:**

    - **IPC Time**: Time spent in inter-process communication between host and backend.
    - **Accelerator Execution Time**: Time taken by the hardware accelerator to execute the graph.

**Use Case:**

- Suitable for developers who want a quick snapshot of performance without deep granularity.
- Helps identify high-level bottlenecks in API usage or backend execution.

**LPAI Basic Profiler**

![LPAI Basic Profiler](data:image/png;base64,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)

## Detailed Profiling

Detailed profiling provides a comprehensive view of the execution behavior of a QNN graph on the LPAI backend. It includes all events captured in basic profiling, along with a richer set of backend-specific metrics. This mode is intended for advanced performance analysis, debugging, and optimization.

**Includes all events from Basic Profiling**, plus:

**Additional Backend-Specific Events:**

- **Inference Preparation Time**: Measures the time spent preparing the inference pipeline before actual execution. This includes memory allocation, data layout transformations, and other setup tasks.
- **Per-Layer Execution Time**: Captures the execution time of each individual layer in the graph. This helps identify performance bottlenecks at the layer level and is useful for fine-tuning model performance.
- **Layer Fusion Information**: Indicates which layers were fused together by the backend for optimized execution. Fusion can reduce memory access overhead and improve throughput.
- **Layer Linking Information**: Provides insights into how layers are connected and scheduled for execution. This can help understand execution dependencies and parallelism opportunities.

These detailed metrics are especially useful for:

- Diagnosing performance regressions
- Understanding backend optimizations
- Identifying layers with high latency
- Verifying the effectiveness of layer fusion and scheduling strategies

**Use Case:**

- Recommended for backend developers and performance engineers.
- Enables root-cause analysis of latency issues and validation of backend optimizations.

**LPAI Detailed Profiler**

![LPAI Detailed Profiler](data:image/png;base64,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)

## Enable Profiling in <cite>qnn-net-run</cite>

To enable profiling, use the <cite>–profiling_level</cite> command-line option:

- <cite>–profiling_level basic</cite>
- <cite>–profiling_level detailed</cite>

A profiling log file will be generated in the output directory:

- The log file is named <cite>qnn-profiling-data_x.log</cite>, where <cite>x</cite> is the execution index.
- A symbolic link <cite>qnn-profiling-data.log</cite> will point to the latest log file.

**Example:**

If the graph is executed three times, the following files will be generated:

- <cite>qnn-profiling-data_0.log</cite>
- <cite>qnn-profiling-data_1.log</cite>
- <cite>qnn-profiling-data_2.log</cite>
- <cite>qnn-profiling-data.log</cite> → <cite>qnn-profiling-data_2.log</cite>

## Visualize Profile Data with <cite>qnn-profile-viewer</cite>

The <cite>qnn-profile-viewer</cite> tool provides a convenient way to visualize profiling data generated by the LPAI backend.
To support extended profiling capabilities for LPAI, the tool dynamically loads the <cite>libQnnLpaiProfilingReader.so</cite> library.

The <cite>libQnnLpaiProfilingReader.so</cite> library parses the LPAI raw profiling output and translates it into a structured, human-readable format.
This enables developers and performance analysts to gain deeper insights into model execution characteristics, identify bottlenecks, and optimize performance across various stages of the neural network pipeline.

**Usage:**

### Push the LPAI profiling related library

$ adb push ${QNN_SDK_ROOT}/lib/${QNN_TARGET_ARCH}/libQnnLpaiProfilingReader.so /data/local/tmp/LPAI
    Copy to clipboard

### Push the qnn-profile-viewer tool

$ adb push ${QNN_SDK_ROOT}/bin/aarch64-android/qnn-profile-viewer /data/local/tmp/LPAI
    Copy to clipboard

### Set up the environment on the device

$ adb shell
    $ cd /data/local/tmp/LPAI
    $ export LD_LIBRARY_PATH=/data/local/tmp/LPAI
    Copy to clipboard

### Execute the profiling viewer by using qnn-profile-viewer

$ ./qnn-profile-viewer --input_log PROFILING_LOG1 --output ./out.csv --reader ./libQnnLpaiProfilingReader.so
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## QNN LPAI Performance Infrastructure

The QNN LPAI Performance Infrastructure provides an interface for clients to control
the performance and power settings of the QNN LPAI Accelerator (eNPU).
This is particularly useful when optimizing inference performance or managing power consumption
in latency-sensitive applications.

The Performance Infrastructure is accessed via the `QnnPerfInfrastructure` interface,
which exposes the `setPowerConfig` function. For LPAI, this maps to
`QnnLpaiPerfInfrastructure_SetPowerConfigFn_t`.

### Performance Modes

Two performance modes are supported:

| Mode | Description |
| --- | --- |
| `DEFAULT` | Normal operating mode. Used for standard inference workloads with a balanced<br>performance and power profile. |
| `BURST` | Burst mode for maximum performance. Typically used to measure peak throughput<br>or to minimize latency in performance-critical tasks. Note: Burst mode may<br>increase power consumption. |

### API Usage

The Performance Infrastructure function is retrieved via `QnnBackend_getPerfInfrastructure`
and then called with a NULL-terminated array of configuration pointers.

The following example sets the eNPU to burst mode with turbo clock:

#include "QnnLpaiPerfInfrastructure.h"
    
    // Obtain the setPowerConfig function pointer
    QnnPerfInfrastructure_SetPowerConfigFn_t setPowerConfig = NULL;
    // ... retrieve via QnnBackend_getPerfInfrastructure ...
    
    // Configure burst mode
    QnnLpaiPerfInfrastructure_EnpuPowerLevel_Config_t enpuPowerConfig =
        QNN_LPAI_PERF_INFRASTRUCTURE_ENPU_POWER_CONFIG_INIT;
    enpuPowerConfig.lpaiPerformanceMode = QNN_LPAI_PERF_INFRASTRUCTURE_PERFMODE_BURST;
    
    QnnLpaiPerfInfrastructure_Config_t perfConfig = QNN_LPAI_PERF_INFRASTRUCTURE_CONFIG_INIT;
    perfConfig.configOption          = QNN_LPAI_PERF_INFRASTRUCTURE_CONFIGOPTION_ENPU_POWER_LEVEL;
    perfConfig.enpuPowerLevelConfig  = enpuPowerConfig;
    
    // Obtain hwVersion (e.g., from QnnDevice_getInfrastructure or backend config)
    uint32_t hwVersion = /* obtained from backend infrastructure */;
    
    const QnnLpaiPerfInfrastructure_Config_t* configs[] = {&perfConfig, NULL};
    setPowerConfig(hwVersion, configs);
    Copy to clipboard

To restore default performance settings after execution, use the `DEFAULT` set mode:

QnnLpaiPerfInfrastructure_EnpuPowerLevel_Config_t clearConfig =
        QNN_LPAI_PERF_INFRASTRUCTURE_ENPU_POWER_CONFIG_INIT;
    clearConfig.lpaiPerformanceMode = QNN_LPAI_PERF_INFRASTRUCTURE_PERFMODE_DEFAULT;
    
    QnnLpaiPerfInfrastructure_Config_t resetConfig = QNN_LPAI_PERF_INFRASTRUCTURE_CONFIG_INIT;
    resetConfig.configOption         = QNN_LPAI_PERF_INFRASTRUCTURE_CONFIGOPTION_ENPU_POWER_LEVEL;
    resetConfig.enpuPowerLevelConfig = clearConfig;
    
    // Obtain hwVersion (e.g., from QnnDevice_getInfrastructure or backend config)
    uint32_t hwVersion = /* obtained from backend infrastructure */;
    
    const QnnLpaiPerfInfrastructure_Config_t* resetConfigs[] = {&resetConfig, NULL};
    setPowerConfig(hwVersion, resetConfigs);
    Copy to clipboard

Note

It is recommended to set `BURST` mode before executing performance-sensitive graphs
and restore to `DEFAULT` afterwards to avoid excessive power consumption.

### Performance Profile in <cite>qnn-net-run</cite>

The `--perf_profile` option in `qnn-net-run` controls the performance mode applied
during execution. The LPAI backend supports the following profiles:

- `burst`
- `high_performance`
- `sustained_high_performance`

Note

Other `--perf_profile` values accepted by `qnn-net-run` (e.g. `balanced`,
`low_power_saver`) are not supported by the LPAI backend and will be ignored.

**Example:**

$ ./qnn-net-run \
        --backend libQnnLpai.so \
        --retrieve_context lpai_graph_serialized.bin \
        --input_list input_list_float.txt \
        --config_file config.json \
        --perf_profile burst
    Copy to clipboard

## QNN LPAI Integration

This section is intended for developers building applications using the QNN Common API and targeting the LPAI backend
Successful integration requires a comprehensive understanding of both QNN and LPAI subsystems, particularly in the areas of memory management and data structure interoperability.

The LPAI backend introduces specific constraints and requirements that differ from other QNN backends. Developers must be familiar with:

- **Memory Allocation Strategies**: LPAI imposes strict limitations on memory usage, necessitating precise control over buffer allocation, alignment, and lifecycle.

Understanding how QNN interacts with LPAI’s memory model is critical for avoiding runtime errors, crashes and optimizing performance.

- **LPAI-Specific Data Structures and Enumerations**: The LPAI API defines a set of custom data types, enumerations, and configuration parameters that must be correctly instantiated and passed to QNN interfaces.

These include tensor descriptors, execution contexts, and backend-specific metadata.

For detailed guidance, refer to the following sections:

- [QNN LPAI Memory Management](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend_memory_allocations.html#qnn-lpai-memory-management)
- [QNN LPAI Data Structures and Enumerations](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend_integration.html#qnn-lpai-data-structures-enums)
- [QNN API Call Flow](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend_integration.html#qnn-api-call-flow)
- Sample App Tutorial

Proper integration ensures compatibility, stability, and optimal performance of your application when deployed on LPAI-enabled hardware.

## QNN LPAI Memory Management

This document describes how the QNN Low-Power AI (LPAI) runtime uses and manages
memory. The runtime relies on user-allocated buffers that must obey backend-provided
alignment constraints. Incorrect alignment or insufficient memory will cause
initialization or execution failures.

- [Overview of Memory Types](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#overview-of-memory-types)
- [Get Memory Alignment Requirements](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#get-memory-alignment-requirements)
- [Scratch Memory](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#scratch-memory)

    - [Key Properties](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#key-properties)
    - [Querying Scratch Memory Requirements](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#querying-scratch-memory-requirements)
    - [Allocating and Configuring Scratch Memory](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#allocating-and-configuring-scratch-memory)
- [Persistent Memory](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#persistent-memory)

    - [Key Properties](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#id17)
    - [Querying Persistent Memory Requirements](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#querying-persistent-memory-requirements)
    - [Allocating and Configuring Persistent Memory](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#allocating-and-configuring-persistent-memory)
- [IO Memory](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#io-memory)

    - [Key Properties](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#id18)
    - [Querying IO Memory Requirements](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#querying-io-memory-requirements)
    - [Allocating and Configuring IO Memory](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#allocating-and-configuring-io-memory)
    - [Shared Buffers in the LPAI Backend](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#shared-buffers-in-the-lpai-backend)
- [Memory Lifetime and Allocation Requirements](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#memory-lifetime-and-allocation-requirements)
- [Recommended Workflow](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#recommended-workflow)
- [TCM Memory Support in LPAIBackendExtensions (ADSP Direct Mode)](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#tcm-memory-support-in-lpaibackendextensions-adsp-direct-mode)

    - [Allocation Details](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#allocation-details)
    - [Configuring TCM Memory via JSON](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#configuring-tcm-memory-via-json)
    - [Invalid `mem_type` Configuration](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#invalid-mem-type-configuration)
    - [Testing TCM Memory Support](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#testing-tcm-memory-support)

### [Overview of Memory Types](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#id63)

The LPAI runtime uses **three distinct memory pools**, each required for correct graph
execution:

1. [Scratch Memory](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend_memory_allocations.html#qnn-lpai-scratch-memory)
2. [Persistent Memory](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend_memory_allocations.html#qnn-lpai-persistent-memory)
3. [IO Memory](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend_memory_allocations.html#qnn-lpai-io-memory)

Each type has unique allocation rules, lifetime characteristics, and backend alignment
requirements.

- **Scratch Memory**: temporary and overwriteable tensors.
- **Persistent Memory**: long-lived tensors such as RNN state.
- **IO Memory**: input/output tensors; may be user-provided or automatically placed
into scratch memory.

All memory pools must be correctly aligned according to backend requirements.

### [Get Memory Alignment Requirements](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#id64)

Before allocating any memory, clients must retrieve backend alignment constraints.
These constraints apply to:

- Scratch memory
- Persistent memory
- User-provided IO buffers

To query backend alignment requirements:

1QnnLpaiBackend_BufferAlignmentReq_t bufferAlignmentReq;
     2
     3QnnLpaiBackend_CustomProperty_t customBackendProp;
     4customBackendProp.option   = QNN_LPAI_BACKEND_GET_PROP_ALIGNMENT_REQ;
     5customBackendProp.property = &bufferAlignmentReq;
     6
     7QnnBackend_Property_t backendProp;
     8backendProp.option         = QNN_BACKEND_PROPERTY_OPTION_CUSTOM;
     9backendProp.customProperty = &customBackendProp;
    10
    11QnnBackend_Property_t *backendPropPtrs[2] = {0};
    12backendPropPtrs[0] = &backendProp;
    13
    14QnnBackend_getProperty(backendHandle, backendPropPtrs);
    15
    16if (!error) {
    17  *startAddrAlignment = bufferAlignmentReq.startAddrAlignment;
    18  *sizeAlignment      = bufferAlignmentReq.sizeAlignment;
    19}
    Copy to clipboard

### [Scratch Memory](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#id65)

Scratch memory holds temporary intermediate results that the runtime can overwrite and
reuse during execution.

#### [Key Properties](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#id66)

- Used for intermediate tensors across graph execution.
- Fully memory-planned offline by the backend.
- Size must be queried from the graph.
- Must be provided before `QnnGraph_finalize()`.
- May be replaced at runtime but must always exist.

#### [Querying Scratch Memory Requirements](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#id67)

QnnLpaiGraph_CustomProperty_t customGraphProp;
    customGraphProp.option   = QNN_LPAI_GRAPH_GET_PROP_SCRATCH_MEM_SIZE;
    customGraphProp.property = scratchSize;
    
    QnnGraph_Property_t graphProp;
    graphProp.option         = QNN_GRAPH_PROPERTY_OPTION_CUSTOM;
    graphProp.customProperty = &customGraphProp;
    
    QnnGraph_Property_t *graphPropPtrs[2] = {0};
    graphPropPtrs[0] = &graphProp;
    
    QnnGraph_getProperty(graphHandle, graphPropPtrs);
    Copy to clipboard

#### [Allocating and Configuring Scratch Memory](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#id68)

QnnLpaiGraph_Mem_t lpaiGraphMem;
    lpaiGraphMem.memType = memType;
    lpaiGraphMem.size    = scratchSize;
    lpaiGraphMem.addr    = scratchBuffer;
    
    QnnLpaiGraph_CustomConfig_t customGraphCfg;
    customGraphCfg.option = QNN_LPAI_GRAPH_SET_CFG_SCRATCH_MEM;
    customGraphCfg.config = &lpaiGraphMem;
    
    QnnGraph_Config_t graphConfig;
    graphConfig.option       = QNN_GRAPH_CONFIG_OPTION_CUSTOM;
    graphConfig.customConfig = &customGraphCfg;
    
    QnnGraph_Config_t *graphCfgPtrs[2] = {0};
    graphCfgPtrs[0] = &graphConfig;
    
    QnnGraph_setConfig(graphHandle, (const QnnGraph_Config_t **)graphCfgPtrs);
    Copy to clipboard

### [Persistent Memory](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#id69)

Persistent memory stores intermediate tensors that **cannot be overwritten**, because
they must persist across operations. Examples include RNN state tensors.

#### [Key Properties](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#id70)

- Holds long-lived intermediate data.
- User must allocate memory after querying required size.
- Must follow backend alignment constraints.
- Must remain valid until `QnnContext_free()`.

#### [Querying Persistent Memory Requirements](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#id71)

QnnLpaiGraph_CustomProperty_t customGraphProp;
    customGraphProp.option   = QNN_LPAI_GRAPH_GET_PROP_PERSISTENT_MEM_SIZE;
    customGraphProp.property = persistentSize;
    
    QnnGraph_Property_t graphProp;
    graphProp.option         = QNN_GRAPH_PROPERTY_OPTION_CUSTOM;
    graphProp.customProperty = &customGraphProp;
    
    QnnGraph_Property_t *graphPropPtrs[2] = {0};
    graphPropPtrs[0] = &graphProp;
    
    QnnGraph_getProperty(graphHandle, graphPropPtrs);
    Copy to clipboard

#### [Allocating and Configuring Persistent Memory](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#id72)

QnnLpaiGraph_Mem_t lpaiGraphMem;
    lpaiGraphMem.memType = memType;
    lpaiGraphMem.size    = persistentSize;
    lpaiGraphMem.addr    = persistentBuffer;
    
    QnnLpaiGraph_CustomConfig_t customGraphCfg;
    customGraphCfg.option = QNN_LPAI_GRAPH_SET_CFG_PERSISTENT_MEM;
    customGraphCfg.config = &lpaiGraphMem;
    
    QnnGraph_Config_t graphConfig;
    graphConfig.option       = QNN_GRAPH_CONFIG_OPTION_CUSTOM;
    graphConfig.customConfig = &customGraphCfg;
    
    QnnGraph_Config_t *graphCfgPtrs[2] = {0};
    graphCfgPtrs[0] = &graphConfig;
    
    QnnGraph_setConfig(graphHandle, (const QnnGraph_Config_t **)graphCfgPtrs);
    Copy to clipboard

### [IO Memory](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#id73)

IO memory contains all graph input and output tensors.

#### [Key Properties](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#id74)

- Can be user-provided or mapped into scratch memory by default.
- User-provided IO buffers must follow alignment requirements.
- Must remain valid during graph execution.

#### [Querying IO Memory Requirements](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#id75)

// QnnSystemInterface is defined in ${QNN_SDK_ROOT}/include/QNN/System/QnnSystemInterface.h
    QnnSystemInterface qnnSystemInterface;
    
    // Init qnn system interface ......
    // See ${QNN_SDK_ROOT}/examples/QNN/SampleApp/SampleAppLPAI code
    // Extract QNN binaryInfo
    const QnnSystemContext_BinaryInfo_t* binaryInfo;
    Qnn_ContextBinarySize_t binaryInfoSize;
    qnnSystemInterface->systemContextGetBinaryInfo(qnnSystemCtxHandle,
                                                   contextBinaryBuffer,
                                                   contextBinaryBufferSize,
                                                   &binaryInfo,
                                                   &binaryInfoSize);
    // Extract graph info from QNN binaryInfo, assume only one graph in the context
    QnnSystemContext_GraphInfo_t* graphInfos = binaryInfo->contextBinaryInfoV1.graphs;
    QnnSystemContext_GraphInfo_t* graphInfo  = &(graphInfos[0]);
    
    // Extract tensor info from graphInfo
    Qnn_Tensor_t* inputs     = graphInfo->graphInfoV1.graphInputs;
    Qnn_Tensor_t* outputs    = graphInfo->graphInfoV1.graphOutputs;
    size_t numInputs         = graphInfo->graphInfoV1.numGraphInputs;
    size_t numOutputs        = graphInfo->graphInfoV1.numGraphOutputs;
    Copy to clipboard

#### [Allocating and Configuring IO Memory](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#id76)

// Qnn_Tensor_t is defined in ${QNN_SDK_ROOT}/include/QNN/QnnTypes.h
    Qnn_Tensor_t tensors[numTensors];
    
    size_t startAddrAlignment, sizeAlignment
    // Retrieve buffer start address and size alignment requirements
    // See ${QNN_SDK_ROOT}/examples/QNN/SampleApp/SampleAppLPAI code
    
    for (uint32_t i = 0; i < numTensors; i++) {
       Qnn_Tensor_t* tensor = &tensors[i];
       tensor->v1.memType   = QNN_TENSORMEMTYPE_RAW;
       int dataSize         = calculate_tensor_size(qnnTensor->v1);
       tensor->v1.clientBuf.data =
          allocate_aligned_memory(startAddrAlignment, sizeAlignment, dataSize);
       tensor->v1.clientBuf.dataSize = dataSize;
    }
    Copy to clipboard

#### [Shared Buffers in the LPAI Backend](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#id77)

In the LPAI backend, *shared buffers* offer an efficient mechanism for moving data
between the host CPU and the LPAI accelerator without requiring additional memory
copies. Shared buffers allow both domains to reference the same underlying memory,
enabling:

- **Zero-copy tensor transfers**
- **Reduced latency during graph execution**
- **Avoidance of redundant CPU-to-accelerator buffer duplication**
- **Improved overall memory efficiency**

Shared buffers are especially valuable when frequently updating input tensors or
retrieving output tensors at high frame rates.

The following tutorial explains how to register and use shared buffers within the
LPAI backend, covering the required API calls and expected memory constraints:

- [Allocate and Use Shared Buffers](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#qnn-lpai-shared-buffer-tutorial)

### [Memory Lifetime and Allocation Requirements](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#id78)

- Scratch and persistent memory must be allocated and provided before
`QnnGraph_finalize()`.
- Persistent memory must remain accessible for the entire lifetime of the LPAI context.
- Scratch memory may be replaced dynamically but must always exist.
- IO memory must remain valid throughout execution.

### [Recommended Workflow](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#id79)

1. Query backend alignment requirements.
2. Query scratch memory size.
3. Query persistent memory size.
4. Allocate aligned memory buffers.
5. Pass scratch and persistent memory to the graph using `QnnGraph_setConfig()`.
6. Call `QnnGraph_finalize()`.
7. Optionally provide user-defined IO buffers.
8. Execute the graph.

### [TCM Memory Support in LPAIBackendExtensions (ADSP Direct Mode)](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#id80)

The `LPAIBackendExtensions` library supports allocating I/O tensor buffers and the
model binary in **Tightly Coupled Memory (TCM)** when running in
[ADSP Direct Mode](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_execution_direct_tutorial.html#qnn-lpai-direct-mode-backend-type). TCM is fast on-chip
memory with lower access latency than main system memory (DDR).

By default, buffers are allocated in DDR. When TCM is selected, allocations are
served from a fixed on-chip memory pool of up to **2,035,712 bytes** (~1.94 MB).

Note

TCM memory support is only available in ADSP Direct Mode, with a maximum pool size
of **2,035,712 bytes** (~1.94 MB). It is not supported on ARM or x86 simulation
platforms.

The following `mem_type` values are supported for I/O tensor buffers and the model binary:

| `mem_type` value | Description |
| --- | --- |
| `"ddr"` | **(Default)** Allocates buffers in main system memory (DDR). |
| `"tcm"` | Allocates buffers in fast on-chip memory (TCM). Lowest latency; limited to ~1.94 MB total. |

#### [Allocation Details](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#id81)

- **I/O tensor buffers** and **model binary**: allocated in the memory type specified
by `mem_type`.
- **Scratch and persistent memory**: always allocated in DDR, regardless of the
configured `mem_type`.
- Memory type cannot be mixed: I/O tensor buffers and the model binary always use
the same type.

#### [Configuring TCM Memory via JSON](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#id82)

Set `mem_type` to `"tcm"` in the `lpai_graph/execute` section of
`eaiParams_direct.conf`:

{
      "lpai_graph": {
        "execute": {
          "mem_type": "tcm"
        }
      }
    }
    Copy to clipboard

Refer to [QNN LPAI Backend Configuration Parameters](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend_setup_configuration.html#qnn-lpai-configuration-parameters)
for the full list of supported configuration keys.

#### [Invalid `mem_type` Configuration](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#id83)

If an unrecognized value is provided for `mem_type`, the config parser logs:

Invalid memory type
    Copy to clipboard

and resolves the type to `QNN_LPAI_MEM_TYPE_UNDEFINED`. When the allocator
encounters this undefined type, it logs:

Memory type only supports DDR & TCM
    Copy to clipboard

and returns `NULL`, causing tensor buffer or model binary allocation to fail and
triggering an initialization error. Ensure `mem_type` is set to `"ddr"` or
`"tcm"`.

#### [Testing TCM Memory Support](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend.html#id84)

##### Prerequisites

- A device with Hexagon ADSP.
- QNN SDK version `v6` or later.
- An offline-prepared LPAI model binary (`*.serialized.bin`).
- Total required TCM memory less than **2,035,712 bytes** (~1.94 MB).
- The required libraries and binaries. Refer to the LPAI entry in the
[Available QNN SDK Backend libraries](https://docs.qualcomm.com/doc/80-63442-10/topic/backend.html#qnn-sdk-backends-table) section.

##### Prepare the TCM Configuration File

Create `eaiParams_direct.conf` with TCM enabled:

{
      "lpai_graph": {
        "execute": {
          "mem_type": "tcm"
        }
      }
    }
    Copy to clipboard

Create `eaiParams_direct.json` for `qnn-net-run`:

{
      "backend_extensions": {
        "shared_library_path": "/data/local/tmp/libQnnLpaiNetRunExtensions.so",
        "config_file_path": "/data/local/tmp/eaiParams_direct.conf"
      },
      "context_configs": {
        "is_persistent_binary": true
      }
    }
    Copy to clipboard

##### Run the Model

$ ./qnn-net-run \
        --input_list model/input_list.txt \
        --backend /data/local/tmp/libQnnLpai.so \
        --direct_mode adsp \
        --config_file model/eaiParams_direct.json \
        --retrieve_context model/tmp.bin
    Copy to clipboard

##### Verify TCM Allocation

With `--log_level debug` or higher, the following `QNN_INFO` messages confirm
successful pool registration and buffer allocation:

TCM pool current size: <current> max_size: <max>
    Allocate size <N> from tcm pool 0
    Copy to clipboard

The first message is emitted once when the TCM pool is registered on the initial
allocation. The second is printed for each subsequent buffer allocation.

If a TCM allocation fails, a `QNN_ERROR` message is emitted:

TCM memory allocation failure: required size: <N>
    Copy to clipboard

##### Limitations and Error Behavior

- TCM is only available in **ADSP Direct Mode**; ARM and x86 simulation modes are
not supported.
- Scratch and persistent memory are always allocated in DDR.
- I/O tensor buffers and the model binary always use the same memory type; mixed
usage across memory types is not supported.
- The TCM pool maximum size is **2,035,712 bytes** (~1.94 MB); there is no
dynamic resizing. If the combined size of all I/O tensor buffers and the model
binary exceeds this limit, the allocator returns `NULL` and emits
`TCM memory allocation failure: required size: <N>`, causing an initialization
failure. Reduce model or tensor buffer sizes, or switch `mem_type` to `"ddr"`.
- The TCM allocator supports a maximum of **32** individual buffer allocations.
Exceeding this limit causes a system fatal error. Reduce the number of individual
tensor buffer allocations, or switch `mem_type` to `"ddr"`.
- In all failure cases, there is no automatic fallback to DDR; the application must
handle the failure explicitly.

## QNN LPAI Data Structures and Enumerations

- [QnnBackend\_Property](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend_integration.html#qnnbackend-property-t)
- [QnnLpaiBackend\_GetPropertyOption](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend_integration.html#qnnlpaibackend-getpropertyoption-t)
- [QnnContext\_Config](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend_integration.html#qnncontext-config-t)
- [QnnContext\_ConfigOption](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend_integration.html#qnncontext-configoption-t)
- [QnnLpaiDevice\_DeviceInfoExtension](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend_integration.html#qnnlpaidevice-deviceinfoextension-t)
- [QnnLpaiGraph\_Mem](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend_integration.html#qnnlpaigraph-mem-t)
- [QnnLpaiMem\_MemType](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend_integration.html#qnnlpaimem-memtype-t)
- [QnnGraph\_Config](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend_integration.html#qnngraph-config-t)
- [QnnLpaiGraph\_CustomConfig](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend_integration.html#qnnlpaigraph-customconfig-t)
- [QnnLpaiGraph\_SetConfigOption](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend_integration.html#qnnlpaigraph-setconfigoption-t)
- [QnnLpaiBackend\_BufferAlignmentReq](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend_integration.html#qnnlpaibackend-bufferalignmentreq-t)
- [QnnLpaiGraph\_CustomProperty](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend_integration.html#qnnlpaigraph-customproperty-t)
- [QnnLpaiGraph\_GetPropertyOption](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend_integration.html#qnnlpaigraph-getpropertyoption-t)
- [QnnLpaiGraph\_CoreAffinity](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend_integration.html#qnnlpaigraph-coreaffinity-t)
- [QnnLpaiGraph\_CoreAffinityType](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend_integration.html#qnnlpaigraph-coreaffinitytype-t)
- [QnnLpaiGraph\_PerfCfg](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend_integration.html#qnnlpaigraph-perfcfg-t)
- [QnnLpaiGraph\_ClientPerfType](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend_integration.html#qnnlpaigraph-clientperftype-t)

### QnnBackend\_Property\_t

This structure provides backend property. This data structure is defined in QnnBackend header file present at `<QNN_SDK_DIR>/include/QNN/`.

| Parameters | Desctiption |
| --- | --- |
| QnnBackend\_PropertyOption\_t option | Option is used by clients to set or get any backend property. |
| QnnBackend\_CustomProperty\_t customProperty | Pointer to the backend property requested by client. |

### QnnLpaiBackend\_GetPropertyOption\_t

This enum contains the set of properties supported by the LPAI backend. Objects of this type are to be referenced through `QnnBackend_CustomProperty_t`.
This enum is defined in QnnLpaiBackend header file present at `<QNN_SDK_DIR>/include/QNN/LPAI/`.

| Property | Desctiption |
| --- | --- |
| QNN\_LPAI\_BACKEND\_GET\_PROP\_ALIGNMENT\_REQ | Used to get the start address alignment and size<br>alignment requirement of buffers.<br>Struct: [QnnLpaiBackend\_BufferAlignmentReq\_t](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend_integration.html#qnnlpaibackend-bufferalignmentreq-t) |
| QNN\_LPAI\_BACKEND\_GET\_PROP\_REQUIRE\_PERSISTENT\_BINARY | Used to query if cached binary buffer needs to be<br>persistent until `QnnContext_free` is called. If yes, then<br>need to specify `QNN_CONTEXT_CONFIG_PERSISTENT_BINARY`<br>during `QnnContext_createFromBinary` |
| QNN\_LPAI\_BACKEND\_GET\_PROP\_UNDEFINED | Unused |

### QnnContext\_Config\_t

The [QnnContext\_ConfigOption\_t](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend_integration.html#qnncontext-configoption-t) structure provides context configuration. This data structure is defined in QnnContext header file present at `<QNN_SDK_DIR>/include/QNN/`.

| Parameters | Desctiption |
| --- | --- |
| QnnContext\_ConfigOption\_t option | Provides option to set context configs.<br>See [QnnContext\_ConfigOption\_t](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend_integration.html#qnncontext-configoption-t) |
| uint8\_t isPersistentBinary | Used with QNN\_CONTEXT\_CONFIG\_PERSISTENT\_BINARY |

### QnnContext\_ConfigOption\_t

This enum defines context config options. This enum has multiple options, but the following option is specific to QNN-LPAI BE.
This enum is defined in QnnContext header file present at `<QNN_SDK_DIR>/include/QNN/`.

| Property | Desctiption |
| --- | --- |
| QNN\_CONTEXT\_CONFIG\_PERSISTENT\_BINARY | Indicates that the context binary pointer is<br>available during `QnnContext_createFromBinary`<br>and until `QnnContext_free` is called. |

### QnnLpaiDevice\_DeviceInfoExtension\_t

`QnnDevice_getPlatformInfo()` uses this structure to list the supported device features/information.
This data structure is defined in QnnLpaiDevice header file present at `<QNN_SDK_DIR>/include/QNN/LPAI/`

| Parameters | Desctiption |
| --- | --- |
| uint32\_t socModel | An enum value defined in Qnn Header that represents SoC model |
| uint32\_t arch | It shows the architecture of the device |
| const char\* domainName | It shows the domain name of the device |

### QnnLpaiGraph\_Mem\_t

`QnnGraph_setConfig()` API used this structure to set custom configs for scratch and persistent buffer.
This data structure is defined in QnnLpaiGraph header file present at `<QNN_SDK_DIR>/include/QNN/LPAI`.

| Parameters | Desctiption |
| --- | --- |
| QnnLpaiMem\_MemType\_t memType | An enum value defined in enum [QnnLpaiMem\_MemType\_t](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend_integration.html#qnnlpaimem-memtype-t)<br>to memory type of buffer. |
| uint32\_t size | Size of buffer |
| void\* addr | Pointer to buffer |

### QnnLpaiMem\_MemType\_t

This enum contains memory type supported by LPAI backend.
This enum is defined in QnnLpaiMem header file present at `<QNN_SDK_DIR>/include/QNN/LPAI`.

| Property | Desctiption |
| --- | --- |
| QNN\_LPAI\_MEM\_TYPE\_DDR | Main memory, only available in non-island mode |
| QNN\_LPAI\_MEM\_TYPE\_LLC | Last level cache |
| QNN\_LPAI\_MEM\_TYPE\_TCM | Tightly coupled memory for hardware |
| QNN\_LPAI\_MEM\_TYPE\_UNDEFINED | Unused |

### QnnGraph\_Config\_t

This structure provides graph configuration.
This data structure is declared in QnnLpaiGraph header file present at `<QNN_SDK_DIR>/include/QNN/`.

| Parameters | Desctiption |
| --- | --- |
| QnnGraph\_ConfigOption\_t option | An enum value defined in `enum QnnGraph_ConfigOption_t`<br>to set custom graph configs. |
| QnnGraph\_CustomConfig\_t customConfig | Pointer to custom graph configs |

### QnnLpaiGraph\_CustomConfig\_t

This structure is used by `QnnGraph_setConfig()` to set backend specific configurations before finalizing the graph.
This data structure is declared in QnnLpaiGraph header file present at `<QNN_SDK_DIR>/include/QNN/LPAI/`.

| Parameters | Desctiption |
| --- | --- |
| uint32\_t option | An enum value defined in [QnnLpaiGraph\_SetConfigOption\_t](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend_integration.html#qnnlpaigraph-setconfigoption-t)<br>set backend specific configs to graph |

| set backend specific configs to graph                          |

### QnnLpaiGraph\_SetConfigOption\_t

This enum contains custom configs for LPAI backend graph.
This enum is defined in QnnLpaiGraph header file present at `<QNN_SDK_DIR>/include/QNN/LPAI`.

### QnnLpaiBackend\_BufferAlignmentReq\_t

This structure contains parameters needed to align the start address of buffer and size of buffer.
This data structure is declared in QnnLpaiBackend header file present at `<QNN_SDK_DIR>/include/QNN/LPAI/`.

| Parameters | Desctiption |
| --- | --- |
| uint32\_t startAddrAlignment | Represents start address alignment of buffer. The start address of the<br>buffer must be startAddrAlignment-byte aligned |
| uint32\_t sizeAlignment | Represents buffer size alignment. The allocated buffer must be a<br>multiple of sizeAlignment bytes |

### QnnLpaiGraph\_CustomProperty\_t

This structure is used by `QnnGraph_getProperty()` API to get backend specific configurations.
This data structure is defined in QnnLpaiGraph header file present at `<QNN_SDK_DIR>/include/QNN/LPAI/`.

| Parameters | Desctiption |
| --- | --- |
| uint32\_t option | An enum value defined in enum [QnnLpaiGraph\_GetPropertyOption\_t](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend_integration.html#qnnlpaigraph-getpropertyoption-t)<br>to retrieve backend specific property. |
| void\* property | Pointer to custom property |

### QnnLpaiGraph\_GetPropertyOption\_t

This enum contains the set of properties supported by the LPAI backend. Objects of this type are to be referenced through `QnnLpaiGraph_CustomProperty_t`.
This enum is defined in QnnLpaiGraph header file present at `<QNN_SDK_DIR>/include/QNN/LPAI/`.

| Property | Desctiption |
| --- | --- |
| QNN\_LPAI\_GRAPH\_GET\_PROP\_SCRATCH\_MEM\_SIZE | Get the size requirement of scratch memory |
| QNN\_LPAI\_GRAPH\_GET\_PROP\_PERSISTENT\_MEM\_SIZE | Get the size requirement of persistent memory |
| QNN\_LPAI\_GRAPH\_GET\_PROP\_UNDEFINED | Unused |

### QnnLpaiGraph\_CoreAffinity\_t

This structure is used by `QnnGraph_getProperty()` to get backend specific configurations.
This data structure is defined in QnnLpaiGraph header file present at `<QNN_SDK_DIR>/include/QNN/LPAI/`.

| Parameters | Desctiption |
| --- | --- |
| QnnLpaiGraph\_CoreAffinityType\_t affinity | Used to set the affinity of selected eNPU core<br>[QnnLpaiGraph\_CoreAffinityType\_t](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend_integration.html#qnnlpaigraph-coreaffinitytype-t) |
| uint32\_t coreSelection | Pointer to custom property |

### QnnLpaiGraph\_CoreAffinityType\_t

This enum contains the possible set of affinities supported by eNPU HW.
This enum is defined in QnnLpaiGraph header file present at `<QNN_SDK_DIR>/include/QNN/LPAI/`.

| Property | Desctiption |
| --- | --- |
| QNN\_LPAI\_GRAPH\_CORE\_AFFINITY\_SOFT | Used to set affinity to soft.<br>Struct: [QnnLpaiGraph\_CoreAffinity\_t](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend_integration.html#qnnlpaigraph-coreaffinity-t). |
| QNN\_LPAI\_GRAPH\_CORE\_AFFINITY\_HARD | Used to set affinity to hard<br>Struct: [QnnLpaiGraph\_CoreAffinity\_t](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend_integration.html#qnnlpaigraph-coreaffinity-t). |
| QNN\_LPAI\_GRAPH\_CORE\_AFFINITY\_UNDEFINED | Unused |

### QnnLpaiGraph\_PerfCfg\_t

This structure is used to set Client’s performance requirement for eNPU Usage. User can configure it before finalizing the graph.
This data structure is declared in QnnLpaiGraph header file present at `<QNN_SDK_DIR>/include/QNN/LPAI/`.

| Parameters | Desctiption |
| --- | --- |
| uint32\_t fps | Used to set frame per second (fps) |
| uint32\_t ftrtRatio | Used to set FTRT ratio |
| QnnLpaiGraph\_ClientPerfType\_t clientType | Used to set client type (Real time or Non-real time)<br>enum: [QnnLpaiGraph\_ClientPerfType\_t](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend_integration.html#qnnlpaigraph-clientperftype-t) |

### QnnLpaiGraph\_ClientPerfType\_t

This enum contains the type of client which can be configured by user before finalizing the graph.
This data structure is declared in QnnLpaiGraph header file present at `<QNN_SDK_DIR>/include/QNN/LPAI/`.

| Property | Desctiption |
| --- | --- |
| QNN\_LPAI\_GRAPH\_CLIENT\_PERF\_TYPE\_REAL\_TIME | Used to set client as REAL TIME.<br>Struct: [QnnLpaiGraph\_PerfCfg\_t](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend_integration.html#qnnlpaigraph-perfcfg-t). |
| QNN\_LPAI\_GRAPH\_CLIENT\_PERF\_TYPE\_NON\_REAL\_TIME | Used to set client as NON-REAL TIME<br>Struct: [QnnLpaiGraph\_PerfCfg\_t](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend_integration.html#qnnlpaigraph-perfcfg-t). |
| QNN\_LPAI\_GRAPH\_CLIENT\_PERF\_TYPE\_\_UNDEFINED | Unused |

## QNN API Call Flow

The integration of a QNN model using the LPAI backend follows a structured three-phase process. Each phase is critical to ensuring the model is correctly initialized, executed, and deinitialized within the QNN runtime environment.

- [Initialization](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend_integration.html#lpai-initialization)
- [Execution](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend_integration.html#lpai-execution)
- [Pause and Resume](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend_integration.html#lpai-pause-resume)
- [Deinitialization](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend_integration.html#lpai-deinitialization)

## Initialization

The initialization phase prepares the QNN runtime and the LPAI backend for model execution.
This phase ensures that all required interfaces, memory resources, and configurations are correctly established before inference begins. It consists of the following key steps:

1. **Interface Extraction**

    Retrieve the necessary interfaces to interact with the QNN runtime and the LPAI backend:

    - **LPAI Backend Interface**

        - Use `QnnInterface_getProviders()` to enumerate available backend providers.
        - Identify the LPAI backend using the backend ID `QNN_LPAI_BACKEND_ID`.
        - This interface is essential for accessing backend-specific APIs and properties.
    - **QNN System Interface**

        - Use `QnnSystemInterface_getProviders()` to obtain system-level interfaces.
        - Provides APIs for managing contexts, graphs, and binary metadata.
2. **Handle Creation**

    Create runtime handles to manage backend and system-level resources:

    - **Backend Handle**: Created using `QnnBackend_create()`, this handle manages backend-specific operations.
    - **System Context Handle**: Created using `QnnSystemContext_create()`, this handle manages system-level context and graph lifecycle.
3. **Buffer Alignment Query**

    Query memory alignment requirements to ensure compatibility with the backend:

    - Use `QnnBackend_getProperty()` with `QNN_LPAI_BACKEND_GET_PROP_ALIGNMENT_REQ`.
    - Retrieve:

        - **Start Address Alignment**: Required alignment for buffer base addresses.
        - **Buffer Size Alignment**: Required alignment for buffer sizes.

    Proper alignment is critical for correctness on hardware accelerators.
4. **Memory Allocation for Context Binary**

    Allocate memory for the context binary, ensuring:

    - Alignment constraints are met.
    - Memory is allocated from the appropriate pool (e.g., Island or Non-Island memory).
5. **Context Creation from Binary**

    Instantiate the QNN context using `QnnContext_createFromBinary()`:

    - The context is immutable and encapsulates the model structure, metadata, and backend configuration.
    - This step effectively loads the model into the runtime.

    Platform-specific configuration requirements:

    - **Island Use Case**: Pass the custom configuration `QNN_LPAI_CONTEXT_SET_CFG_ENABLE_ISLAND` to enable island execution.
    - **Native ADSP Path**: Use the common configuration `QNN_CONTEXT_CONFIG_PERSISTENT_BINARY` to enable persistent binary support.
    - **FastRPC Path**: No additional configuration is required.
6. **Graph Metadata Retrieval**

    Use `QnnSystemContext_getBinaryInfo()` to extract metadata embedded in the binary:

    - Graph names
    - Versioning information
    - Backend-specific metadata
7. **Graph Retrieval**

    Retrieve the graph handle using `QnnGraph_retrieve()`:

    - Pass the graph name obtained in the previous step.
    - The graph handle is used for further configuration and execution.

Note

The following steps are specific to the Hexagon (aDSP) LPAI backend and are required for proper memory and performance configuration.

8. **Scratch and Persistent Memory Allocation**

    Query memory requirements using `QnnGraph_getProperty()`:

    - `QNN_LPAI_GRAPH_GET_PROP_SCRATCH_MEM_SIZE`: Temporary memory used during inference.
    - `QNN_LPAI_GRAPH_GET_PROP_PERSISTENT_MEM_SIZE`: Memory required across multiple inferences.

    Allocate memory accordingly, ensuring alignment and memory pool selection.
9. **Memory Configuration**

    Configure the graph with allocated memory using `QnnGraph_setConfig()`:

    - `QNN_LPAI_GRAPH_SET_CFG_SCRATCH_MEM`
    - `QNN_LPAI_GRAPH_SET_CFG_PERSISTENT_MEM`

    This step binds the allocated memory to the graph for runtime use.

    See [QNN LPAI Memory Management](https://docs.qualcomm.com/doc/80-63442-10/topic/lpai_backend_memory_allocations.html#qnn-lpai-memory-management) for more details.

10. **Performance and Core Affinity Configuration**

    Optimize execution by configuring:

    - **Performance Profile**: `QNN_LPAI_GRAPH_SET_CFG_PERF_CFG` (e.g., balanced, high-performance, low-power)
    - **Core Affinity**: `QNN_LPAI_GRAPH_SET_CFG_CORE_AFFINITY` (e.g., assign execution to specific DSP cores)

    These settings help balance performance and power consumption.
11. **Client Priority Configuration**

    Set the execution priority of the graph using:

    - `QnnGraph_setConfig(QNN_GRAPH_CONFIG_OPTION_PRIORITY)`

    This is useful in multi-client or multi-graph environments where scheduling priority matters.
12. **Graph Finalization**

    Finalize the graph using `QnnGraph_finalize()`:

    - Locks the graph configuration.
    - Prepares internal structures for execution.
    - Must be called before any inference is performed.
13. **Tensor Allocation**

    Retrieve and prepare input/output tensors:

    - Use `QnnGraph_getInputTensors()` and `QnnGraph_getOutputTensors()`.
    - Set tensor type to `QNN_TENSORTYPE_RAW`.
    - Allocate and bind client buffers to each tensor.

    Proper tensor setup ensures correct data flow during inference.

**LPAI Initialization Call Flow**

![LPAI Initialization Call Flow](data:image/png;base64,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)

## Execution

The execution phase is responsible for running inference using the finalized QNN graph. This phase is typically repeated for each inference request and involves the following steps:

1. **Input Buffer Preparation**

    - Populate the input tensors with data from the client application.
    - Ensure that the data format, dimensions, and layout match the model’s input specification.
    - Input tensors must be bound to client-allocated buffers, typically of type `QNN_TENSORTYPE_RAW`.
2. **Graph Execution**

    - Invoke the model using `QnnGraph_execute()`.
    - This function triggers the execution of the graph on the target hardware (e.g., eNPU).
    - The execution is synchronous; the function returns only after inference is complete.

    **Execution Flow:**

    - Input data is transferred to the backend.
    - The backend schedules and executes the graph operations.
    - Intermediate results are computed and stored in backend-managed memory.
    - Final outputs are written to the output buffers.
3. **Output Retrieval**

    - After execution, output tensors contain the inference results.
    - These results are available in the client-provided output buffers.
    - The application can now post-process or consume the output data as needed.
4. **Optional: Profiling and Logging**

    - If profiling is enabled (via <cite>–profiling_level</cite>), performance data is collected during execution.
    - Profiling logs are written to the output directory and can be visualized using <cite>qnn-profile-viewer</cite>.
5. **Error Handling**

    - Check the return status of `QnnGraph_execute()`.
    - Handle any runtime errors, such as invalid inputs, memory access violations, or hardware faults.

Important

- Input and output buffers must remain valid and accessible throughout the execution.
- Ensure that memory alignment and size requirements are met to avoid execution failures.

**LPAI Execution Call Flow**

![LPAI Execution Call Flow](data:image/png;base64,UklGRhQSAABXRUJQVlA4TAcSAAAva4IxABXhef5/chxHfBkOHTLkS3DosEOHfAkTdjghQ4UTMlTY4YQKJ5yww4YjhQ6dEfj/jn+xit3uKo9G4cykPij8IIFeEL4PwVeDvo/6J405yke0933fNOagRnufbJjbVErGKoB7EXC3KhG5N7UXCXTQmRooCNBu79EVCL4YsNCGItt22OZAL8PMgoGBgWEW/FBLEwyUWcwKCwUVlrAuQUWBcW17JufGh3qCxdq+uLhYXGvwxflZxbc2mFpqWxsM9rszFgwG+y8LkmQhYeYMVbwANfc8H6KGl+8SPLW46D9vVWMNBTxM9sZkFe6XN+MMnnY4zqGcP0+vSXbH5HG4Xh7G45I4HI/QCqxTIHmjO7tDJm6XP3iUZNvhkKp1Jg5Qdbu89zF6UkIEAh0J2t09ukIkVSg6Da2PIB/bEFnKJQNjii2LrBnZ+Fh+uBRvNcPXfpKFh4jrRpZzMcbcE5Fehp17nmVbmtnha++0ZstklprLtrSnu4K8Hr6vyOfcumUSRsVI5FI3FnsA8+7aX5Jz6y2zsmsdaYzEZSONjHwM1RhLz4woi9tCkXY2WPKx3BzIJLstcjCs2Qkua+l1ryiYsViYodmFI000Wh4Md6WXHXxmVzBZdGOGZre4VlWriG1osQ9awsKsoHP5CDSWuj5jwmPpgYW7pjEScqRedzmV/WNMsWlYY2jUDoa7jgCVjU/iFDK0KsLj9nQkaOHK6XXjXnftPaY1zG4zPMbIQSbbbbk0Em7FMUgm0iSn8obrJ1aFjFE6hCb8XSb5MbeJkysHQ1APj10+Ingllkb3VczbWsV2zuYPhbSnBuIeXohs274J2o9dQFBc+iZvWxp+MNxFHAyXgJFedlg2fSjQ9iKX0FCLRO0/0MHwHt8sDd2KuPADNUw3RkjTENpTA/RgyINWDY59hiwNWBcCSvKKuPDPkI3pxoj2FLMtTahOUEAj40iDc9+NiTAKVMOVfzcmxaQrtApx5TFFyakxmt7Pd/4i3tpvmS6SwSN4TBK7Y5XOwO1ydJbE6ShrEKyu0DmyN1ak6ucjtxuTIn3D2bhTLetOM4/hUdgbfxC54Dg61EblF2b7MPIsolZ2JhfqsnXkXXAYPflfnMeRd8EfeGuf612gTPR5t8hGW5Ar+2qMKVS3QS4snVBWt3o30ZCPSLUaFUUhIteX0geg5MiVtvJ4spQ1MdLCCjJ9oDW15BPka7bYa7OW6YzNYrWdos1ijBerz1PkT9INDEulqwtHiCLbGOerK6U02iwGbEsrJx9hTAoKhSXMYi8aq+qa5eZSp6NKfk6FPzHmQb3kLRMY5RAYJYerw5gUKXUIhaoT00eyyk+jMs712XpQR9lhrQd1kis6Uj9C9mnEJDE2i/zcuEWS/50tmjXJSWdEOgzKFGxAsHQQdGguTBcxi7Exue51ZfFuYwYgMcAUKidgMLs7hJ4Ht8I1jBnQrkhyc5oUHMPhZuvdwJtAhp2P7tEKCEsns1YnmrXy1WEY28mE7dGRZOckjfIRinxEXyl00OthjDS4IShR9bDYwyz2wE61gw2vAmaQEmQHaSUpsYBy8nNlqjxVYplwSAgpKiF/wkK65W6CO4I10nQYgQy8JBc8OKLeJMw+qgwGitP6ePEit1i8uHPSoUA7UkoGEdglp/UIPyFv0diqyN24cyFbq30oelyBHVV2VI3o8cUTBQxIx5+kmD5SGodIVqsCdmCKiifiLZhM0KvDn3RwjbH0ada4/aN7OiPaYRbQvqsgBDgrlTlULccgUg5aaFeAFbAZOxC/oPUW9GHAQoz7ZJ0p11nLGINA5zLDgGogSggpxqhY7GlUHtRBGBkHBIP9A+bQHYUxoBFoSRnGIeoaK0YWILmQjtVgjDHhUFnoSC1BuwJ37axlTuubLthmOGWyCzw0Bfdx0dIhzJ8wxQP1V94i6dPq5FQoqODwgrFgFKDe6IA11qNdaCEdhtg5oZBcLCxor1kATtSYgEFrHinLTddnyJrd+mwzGsrQuzvuZZCEiLFh2Dmx2AyGpBkyYVVQwWLjFBdjH1uggvN/F6oTv9j6Knlk7k+0aJo1MB2G+sBQaC5RRFLuIEjip3ULhEP0Amkpv1NN86yFfHH5p5ssbmxF9IFPw9xu+Rno4pIdEWct881MeAKCfTRYQcmacGVZiVchwsJu5AKXrqML1JQmkuAsx4bu1o/IRSo6RXaIBVYrdL1Hz84Js5uu+kmHIft8PWPBYURzIflrdwuHzGLM7paizlwUNGbBdEWCld7SrRytoxTROY+eqComknT9dNgmMdYhcNrcgLnw5r9B0zy0K1w4RVTigR6CXfE6nIWX9hvN6hOu2LrqXVCZheZdEF2gzHls0R+hX8vCa+Bq5R7oqBq+Fnl4Y5/rff7a34SvwXwZkRfG9FfC+bPPzsdbZcbYIyKHZyHmf1NufP29XhCfvxEdPjUPn3329bjvPVytVu4Pmr4ewsMrC9j860de2q8134DI8+Lzn488i7jhrd079QJl7re9jPliLlQOzX3frDFrGbPYs37LI4dvXxSkGHuS8icde1K28Q66lZNEkjqTeGUkM2ulWHEnU+hEEboxXXBaR0YffVGXRoYP0V0RsddOptiLRDWKkgFiZS+D9fGN2FVabos9qzfeGpsOmk+hIpm9p+4kUVRRRNEGTVg6sA2TlkuUWHhS31vBB9o56eD98BQD/YhOU+BPUlJgs4/mZqMzKZDJC8jsCXwdGny7UubGzx1+HoOdm3toQgTmLpbYavFqv5ykWH7QMqmmDMa5brnUlCBjlKDs/Am4JOzJgfYh7RDsmY8w4dBCEi8q80JHm8SLkMRAUzGkZCAJ6FTiSm57acmVJEXvMY2nbQjM4kWHk5nd/eeBbXzroy+Wyyxn0Q83tYq/1z2+Xzy62S6ZpoCdcAEsyEwKdPICIof6kmiD8rjv54qVTqgFN48BjAn0Rs7+pI9unAsOOyd9O5Ki/aHBdJS/IiydoLtAM68kMkMDf+6atToUlAEUUWohiQEkMSw3iQAlwdVrSlxfJLeOtuRKkuIA9UGUDJxJhqV0vN7cs4s0GJSLymcdMQ4RAZDie8X32ZsvG5Ny1R70wYGTwzDDMZpQJWUsyHmIhd7SG60HfUjEjqRIf/BAZ5GeAg0tmucYapclRQr5CB3hMAoG9iQBYgMW6kqw5IqTYsFywcDZZEBS1axdKDXr0NX1wB9YWXQwKUCLL8FlQWZSgMoncizMvAOgyFj4Ez1kMoMEPWX5CETZSRFxCzyoYzp6osDozvM6OjrYWrIjCX+iCLR0WCwlDkY6V4IjyWhQcvkcNhGt7OV0dan2U7mB7ktfWFztp8efpDwBKisyeYEFbu6RE0FhaX3OImGYfRRBJzNgF5aSshcQROycIDniaY2gXOxKIkowwSkBtZOhknXVQV1ZnE0GSa7sJfxn+uwkZeJPvgWawCxzMaush+B2W92Uv4TpR1roboZj56RPJy/QQqdCCNIo6VALZh6DfhQsELiAH9TZZmtJ2cqDOkllbBDI5nf3LNDq+BMbOMVJnHJJDCAJC2/pWh1/giCuL0ZjmD1rwLpqwa48ziTDQy5tk9M6PMVlga+WsVxyWnqhB/JZd5/r/aD1oK4Fn4MXe+SETGZSIJMX6PEnxqBqAXM8MQJ3adcy31QyOyd4tzBhdgvv960lZZmgb4V3G9NhZlLwJ4s9lDJ6R8ICOS7+olz4JPLVctFQEi/iJLiKTAw/50JgUuyKjMKCGHCuqAxZiKsOh5Lh2PlAiqyZjxqMOa3PWsag6+WUgl9G6Nk32bH8V6QwoIqEzQDvtkEFKp7KsKdTc6S2JrWTn05N+Oq3HPmYDnSPP+nYkHCFJe5IMvnAvmRmheFR+aFbbx5V9i+Mz6MiGGeAou/EG4YUf01ib1LjjrNlqc2whXI6rZeHlYQrKXF/ghJfvGhzMnke2UYhPvbsQPN9xY/Of/Ta58nVqgf2u7mHMP96FuMjz+w3muP5s8/C5pXJeAmeQ4TPqgjnlcm07TVEbQ5/nppGtcrcxGOI94bPfj0sAI6wFXleMZ3Pn3pv5JXFrXE7egJmtA+9C2oXKLPlYdww1YUNfkfvPdxA472PmJHLi+Zj+qp9A40VfXmOounyOGue1/0NNNbsf1bUp66P0vd3wwz/5qP6m+2V/rh/tAmm9P3bIAPw+cc5VOSBvAVKftlHzbURoyi32vVeSsOCKpOCRRk7krFqxhHDw+0R+zjKq7cYvHQesQVVJoXYooz1ZMpPlwM93B2xj/6lsSnUi0/HyDhmj4Tb/ZKXTNhqk8+Ni7Ef5OujX/KeK23lYT7SYHUSk+vXVZFJuXQcYMgZ+atb/N9nkSNOhHPyg8ul1+EzerwWNfFX9gCzmABBH7WKnJxok8wdNODRMTL2ffZIuB1BTFiQBqMT+9d/d7Xh42CNALAnNpwTgejxWsTkPwCwBIrGzTUd4uYZ4wwqtyA3zaDBK9nCdTvNwEBp3Tox+59FVRbcuT4xGQ8zf95SJkh1aoxyKfTRglGYeMbqz8SYb6PRMz+HMSoZPzDmmbtkgYftb2M5ZPtMft4QSPwTl5vXJ7k5lNAUyeGcSv+HIp/C7JeOT8jp/Nw//Gc4zuoPmejv3Bworv/ubLrl+/OPzq5P8HAZ3CGgxa2Tb/sJlCVdXG5OoYQ/eld5gg1yVVCj6f5n/SRfLwQ6siaHv4JsM4lvgx6U760PKAXg8FcgCT8Ah/1n57+pzDP4Nuj8Bz/m/qzLIwNjYLj05+/8v8+iMypUNj7jsqCSVTYcW0rnLvX0YwZklCmGT+PBHaYZChgKGulNGVusAGyhYxOHX/GTrV+ZqtZAwb6XrFw+f2NT3Yya8AnXmP3PQkXHsQb0WrjL8W2Aemr4NoBO04ZZoG0V+AIo4Ago3/bxCy47MnJAXG4iuEtiwhrc2253fHK1ma/3uZgz3w9wybOLhHC5ia8+tJCXSKoHipddoBqm46xmweFP95+li+uTsngEbg4UtzKRPzvPzWvpc0AzvB2Ed4UBvN+b3TrhmgPYkAc147eP1rj1AbqAU7MKaJ63lNcHdM0c2OFyE5rdKBkYg65nv41bp+hUTK52/cTsFzxDQaPd8Dq6ZL1DIJ7WFrc+YPZXxOUmhh77+sTQq+af+ird7ZmCjQAX2itj+79n0RX0t32XifZjtTgmn+tZOBLejqwzrjFrRmx4I18T+s/1qG5MRlmcAw5U735M7P8jXRfcWMMm+XtYJtKUfF9h4UjMdrHmu1VOkNrwRrE2NPJMpv68RT4YjKkNfk8Z00RSKxxV5IHc6u9Lyv+eNtZ5WCLySrDnRyng4UbZeMPVsRFH5bL1B5tcaqv28/YNOc6YQBHu60DnzNrfbczx+Sda3kfkqnfBtejJ/+L+lnfBVW/tc70LfblatRjL9GNVq/EHLvhqtIBqmbGMarnx3jI4KpDYHWfoquulKk7HWbpmnYls2x3ytOudNuEbsu1wSLWSqLreiZff9aSECAQ6ErS7e3SFSKpQdBpaH0E+tiGylEsGxhRbFlkzsvFxT4q3muHHFh4irhvpGWPuiUhvcVu2pdlde6eDTGYk29Ke7gryevi+Ip+3tYyKkcilbiz2AObdK/JDn9m1jjRG4rKRxv4xVGMsPTOiLG4LRdrZ4E+WmwMfvS1yMKzZCS5r6XWvKJixWJih2YUjTTRaHgx3pZcdfGZXMFl0Y4Zmt7hWVauIbWixD1rCwqygc/kINJa6PmPCY+mBhbumMRJypF53OZX9Y0yxaVhjaNQOhruOAJWNT+IUMrQqwuP2dCRo4crpdeNe94VpDbPbDI8xcvCLt+XSSLgVxyCZSJOcyhuun1gVMkbpEJrwd5nkx9wmTq4cDEE9PHb5iOCVWBrdVzFva/1bNn8opD01EPfwQmTb9k3QfuwCguLSN3nb0vCD4S7iYLgEjPT++4cVaHuRS2ioRaL2H+hgeI9vloZuRVz4gRqmGyOkaQjtqQF6MORBqwbHPkOWBqwLASV5RVz4Z8jGdGNEe4rZliZUJyigkXGkwbnvxkQYBarhyr8bk+9coVWIK48pSk6N0fR+vvMX8dZ+y3SRDIo4J4nd8TidgdvlqEjidJT1j+Srf3dWsjf+rhq53ijQz/24s/Gr1ftl3dX+yN7Yilxw3D9yNlYReWpx0X8ugkQEAA==)

## Pause and Resume

The LPAI backend supports pausing and resuming a finalized graph to temporarily release hardware
resources without destroying the graph. When paused, the performance related resources acquired
during `QnnGraph_finalize()` are released while the graph and its loaded weights remain valid.
When resumed, those resources are re-acquired and the graph is ready to execute again.

Note

Pause and resume are only supported for graphs compiled in **non-island mode**.

The following steps describe the pause and resume flow:

1. **Pause the Graph**

    - Call `QnnGraph_setConfig()` with `QNN_LPAI_GRAPH_SET_CFG_PAUSE_EXECUTION` to signal
the intent to pause.
    - Call `QnnGraph_finalize()` to apply the change. After finalize completes, the performance
related resources are released.
2. **Resume the Graph**

    - Call `QnnGraph_setConfig()` with `QNN_LPAI_GRAPH_SET_CFG_RESUME_EXECUTION` to signal
the intent to resume.
    - Call `QnnGraph_finalize()` to apply the change. After finalize completes, the performance
related resources are re-acquired.
3. **Execute After Resume**

    - Once the graph is successfully resumed and re-finalized, invoke `QnnGraph_execute()` or
`QnnGraph_executeAsync()` as normal.

Important

- `QnnGraph_finalize()` must be called after each `QnnGraph_setConfig()` for pause or
resume to take effect.
- Calling `QnnGraph_execute()` on a paused graph without first resuming and re-finalizing
will return an error.

**LPAI Pause and Resume Call Flow**

![LPAI Pause and Resume Call Flow](data:image/png;base64,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)

## Deinitialization

The deinitialization phase is responsible for releasing all resources allocated during the initialization and execution phases.
Proper deinitialization ensures that memory is freed, handles are closed, and the system is left in a clean state. This is especially important in embedded or resource-constrained environments.

The following steps outline the deinitialization process:

1. **Release QNN Context Handle**

    - Call `QnnContext_free()` to release the context created via `QnnContext_createFromBinary()`.
    - This step invalidates the context and all associated graph handles.
2. **Release LPAI Backend Handle**

    - Call `QnnBackend_free()` to release the backend handle created during initialization.
    - This step ensures that backend-specific resources (e.g., device memory, threads) are properly cleaned up.
3. **Release QNN System Context Handle**

    - Call `QnnSystemContext_free()` to release the system context.
    - This step finalizes the system-level interface and releases any associated metadata or configuration.
4. **Free Scratch and Persistent Memory**

    - If memory was allocated manually for scratch and persistent buffers (e.g., on Hexagon aDSP), it must be explicitly freed.
    - These buffers are typically allocated based on properties queried via `QnnGraph_getProperty()`.
5. **Free Input and Output Tensors**

    - Release memory associated with input and output tensors.
    - This includes:
- Client-allocated buffers bound to tensors
- Any metadata or auxiliary structures used for tensor management
6. **Optional: Logging and Diagnostics Cleanup**

    - If profiling or logging was enabled, ensure that any open file handles or logging streams are closed.
    - Optionally, flush logs or export profiling data before shutdown.

Important

- All deinitialization steps must be performed in the reverse order of initialization to avoid resource leaks or undefined behavior.
- Failure to properly deinitialize may result in memory leaks, dangling pointers, or device instability.

**LPAI Deinitialization Call Flow**

![LPAI Deinitialization Call Flow](data:image/png;base64,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)

## QNN LPAI Shared Buffer Tutorial

### Introduction

In the LPAI backend, shared buffers provide an efficient mechanism for exchanging
data between the host CPU and the LPAI accelerator without incurring costly memory
copies. This tutorial demonstrates how to register and use shared buffers in the
LPAI backend to achieve zero-copy data transfers and optimized memory usage across
processing domains.

### Supported Shared Memory Type

| Qnn\_MemType\_t | Supported | MemInfo Struct | Description |
| --- | --- | --- | --- |
| QNN\_MEM\_TYPE\_ION | Yes | Qnn\_MemIonInfo\_t | Entire buffer is represented by a single file descriptor. Assumes the buffer starts at the beginning of the fd, no offset handling. |
| QNN\_MEM\_TYPE\_CUSTOM | Yes | QnnLpaiMem\_MemInfoCustom\_t | Allows partial mapping of a shared memory region (e.g., multiple buffers share the same fd with different offsets). |
| QNN\_MEM\_TYPE\_DMA\_BUF | No | Not Applicable | Not Applicable |

Note

This tutorial is only focused on the shared buffer usage. There are some prerequisites in the
SDK example code not discussed in detail here. Users can refer to the corresponding part in the
QNN documentation, or refer to the SampleApp.

SampleApp code: ${QNN\_SDK\_ROOT}/examples/QNN/SampleApp/SampleAppLPAI

### Loading prerequisite shared libraries to use the RPCMem framework

A hardware device equipped with a Qualcomm chipset includes a shared library which provides the
functions for shared buffer manipulation.

#### Loading shared library

The `libcdsprpc.so` shared library is available on most mainstream Qualcomm chipset equipped
devices (SD888 and later).

We can dynamically load it as shown below:

1 void* libCdspHandle = dlopen("libcdsprpc.so", RTLD_NOW | RTLD_LOCAL);
    2
    3 if (nullptr == libCdspHandle) {
    4   // handle errors
    5 }
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#### Resolving Symbols

After the shared library is successfully loaded, we can proceed to resolve all necessary symbols.

The below code snippet shows a template to resolve a symbol in a shared library:

1/**
     2* Definition: void* rpcmem_alloc(int heapid, uint32 flags, int size);
     3* Allocate a buffer via ION and register it with the FastRPC framework.
     4* @param[in] heapid  Heap ID to use for memory allocation.
     5* @param[in] flags   ION flags to use for memory allocation.
     6* @param[in] size    Buffer size to allocate.
     7* @return            Pointer to the buffer on success; NULL on failure.
     8*/
     9typedef void *(*RpcMemAllocFn_t)(int, uint32_t, int);
    10
    11/**
    12* Definition: void rpcmem_free(void* po);
    13* Free a buffer and ignore invalid buffers.
    14*/
    15typedef void (*RpcMemFreeFn_t)(void *);
    16
    17/**
    18* Definition: int rpcmem_to_fd(void* po);
    19* Return an associated file descriptor.
    20* @param[in] po  Data pointer for an RPCMEM-allocated buffer.
    21* @return        Buffer file descriptor.
    22*/
    23typedef int (*RpcMemToFdFn_t)(void *);
    24
    25RpcMemAllocFn_t rpcmem_alloc = (RpcMemAllocFn_t)dlsym(libCdspHandle, "rpcmem_alloc");
    26RpcMemFreeFn_t rpcmem_free = (RpcMemFreeFn_t)dlsym(libCdspHandle, "rpcmem_free");
    27RpcMemToFdFn_t rpcmem_to_fd = (RpcMemToFdFn_t)dlsym(libCdspHandle, "rpcmem_to_fd");
    28if (nullptr == rpcmem_alloc || nullptr == rpcmem_free || nullptr == rpcmem_to_fd) {
    29    dlclose(libCdspHandle);
    30    // handle errors
    31}
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### Data Buffer Alignment Requirement

The LPAI backend has requirements on data buffer start address and size alignment. These alignment
requirements apply to shared memory buffers. Please refer to the code in
`${QNN_SDK_ROOT}/examples/QNN/SampleApp/SampleAppLPAI` for how to query alignment requirements.

### Using QNN\_MEM\_TYPE\_ION with QNN API

The following is the representation of ION shared memory, where each tensor has its own shared buffer
with its own unique memory pointer, file descriptor, and memory handle.

![../../_static/resources/ION_Shared_Buffer.png](data:image/png;base64,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)

An example is shown below:

LPAI Shared Buffer Example

1// QnnInterface_t is defined in ${QNN_SDK_ROOT}/include/QNN/QnnInterface.h
     2 QnnInterface_t qnnInterface;
     3 // Init qnn interface ......
     4 // See ${QNN_SDK_ROOT}/examples/QNN/SampleApp code
     5
     6 // Qnn_Tensor_t is defined in ${QNN_SDK_ROOT}/include/QNN/QnnTypes.h
     7 Qnn_Tensor_t inputTensor;
     8 // Set up common setting for inputTensor ......
     9 /* There are 2 specific settings for shared buffer:
    10 *  1. memType should be QNN_TENSORMEMTYPE_MEMHANDLE; (line 50)
    11 *  2. union member memHandle should be used instead of clientBuf, and it
    12 *     should be set to nullptr. (line 51)
    13 */
    14
    15 size_t startAddrAlignment, sizeAlignment
    16 // Retrieve buffer start address and size alignment requirements
    17 // See ${QNN_SDK_ROOT}/examples/QNN/SampleApp/SampleAppLPAI code
    18
    19 #define MAKE_MULTIPLE(i, m) (((i) % (m)) ? ((i) + (m) - ((i) % (m))) : (i))
    20 #define PAD_TO_VALUE(addr, alignment) ((((size_t)addr) + (alignment - 1)) & ~(alignment - 1))
    21
    22 size_t bufSize;
    23 // Calculate the bufSize base on tensor dimensions and data type ......
    24 bufSize = MAKE_MULTIPLE(bufSize, sizeAlignment);
    25 // Make the bufSize aligned to sizeAlignment
    26
    27 #define RPCMEM_HEAP_ID_SYSTEM 25
    28 #define RPCMEM_DEFAULT_FLAGS 1
    29
    30 // Allocate the shared buffer
    31 // Allocate extra memory to accomodate startAddrAlignment requirement
    32 uint8_t* memPointer = (uint8_t*)rpcmem_alloc(RPCMEM_HEAP_ID_SYSTEM, RPCMEM_DEFAULT_FLAGS, bufSize + startAddrAlignment);
    33 if (nullptr == memPointer) {
    34     // handle errors
    35 }
    36 uint8_t* alignedMemPointer = PAD_TO_VALUE(memPointer, startAddrAlignment);
    37
    38 int memFd = rpcmem_to_fd(alignedMemPointer);
    39 if (-1 == memfd) {
    40     // handle errors
    41 }
    42
    43 // Fill the info of Qnn_MemDescriptor_t and regist the buffer to QNN
    44 // Qnn_MemDescriptor_t is defined in ${QNN_SDK_ROOT}/include/QNN/QnnMem.h
    45 Qnn_MemDescriptor_t memDescriptor = QNN_MEM_DESCRIPTOR_INIT;
    46 memDescriptor.memShape = {inputTensor.rank, inputTensor.dimensions, nullptr};
    47 memDescriptor.dataType = inputTensor.dataType;
    48 memDescriptor.memType = QNN_MEM_TYPE_ION;
    49 memDescriptor.ionInfo.fd = memfd;
    50 inputTensor.memType = QNN_TENSORMEMTYPE_MEMHANDLE;
    51 inputTensor.memHandle = nullptr;
    52 Qnn_ContextHandle_t context; // Must obtain a QNN context handle before memRegister()
    53 // To obtain QNN context handle:
    54 // Refer to ${QNN_SDK_ROOT}/docs/general/sample_app.html#load-context-from-a-cached-binary
    55 Qnn_ErrorHandle_t registRet = qnnInterface->memRegister(context, &memDescriptor, 1u, &(inputTensor.memHandle));
    56 if (QNN_SUCCESS != registRet) {
    57     rpcmem_free(memPointer);
    58     // handle errors
    59 }
    60
    61 /**
    62 * At this place, the allocation and registration of the shared buffer has been complete.
    63 * On QNN side, the buffer has been bound by memfd
    64 * On user side, this buffer can be manipulated through memPointer.
    65 */
    66
    67 /**
    68 * Optionally, user can also allocate and register shared buffer for output as adove codes (lines 22-59).
    69 * And if so the output buffer also should be deregistered and freed as below codes (lines 79-83).
    70 */
    71
    72 // Load the input data to memPointer ......
    73
    74 // Execute QNN graph with input tensor and output tensor ......
    75
    76 // Get output data ......
    77
    78 // Deregister and free all buffers if it's not being used
    79 Qnn_ErrorHandle_t deregisterRet = qnnInterface->memDeRegister(&tensors.memHandle, 1);
    80 if (QNN_SUCCESS != registRet) {
    81     // handle errors
    82 }
    83 rpcmem_free(memPointer);
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### Using QNN\_MEM\_TYPE\_CUSTOM with QNN API

The following is the representation of a Multi-Tensor shared buffer where a group of
tensors is mapped to single shared buffer. This single shared buffer has one memory
pointer and a file descriptor; however each tensor has its own memory pointer offset and
memory handle.

![../../_static/resources/Multi_Tensor_Shared_Buffer.png](data:image/png;base64,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)

An example is shown below:

LPAI Multi-Tensor Shared Buffer Example

1// QnnInterface_t is defined in ${QNN_SDK_ROOT}/include/QNN/QnnInterface.h
      2 QnnInterface_t qnnInterface;
      3 // Init qnn interface ......
      4 // See ${QNN_SDK_ROOT}/examples/QNN/SampleApp code
      5
      6 // Total number of input tensors
      7 size_t numTensors;
      8
      9 // Qnn_Tensor_t is defined in ${QNN_SDK_ROOT}/include/QNN/QnnTypes.h
     10 Qnn_Tensor_t inputTensors[numTensors];
     11 // Set up common setting for inputTensor ......
     12 /* There are 2 specific settings for shared buffer:
     13 *  1. memType should be QNN_TENSORMEMTYPE_MEMHANDLE; (line 40)
     14 *  2. union member memHandle should be used instead of clientBuf, and it
     15 *     should be set to nullptr. (line 41)
     16 */
     17
     18 size_t startAddrAlignment, sizeAlignment
     19 // Retrieve buffer start address and size alignment requirements
     20 // See ${QNN_SDK_ROOT}/examples/QNN/SampleApp/SampleAppLPAI code
     21
     22 #define MAKE_MULTIPLE(i, m) (((i) % (m)) ? ((i) + (m) - ((i) % (m))) : (i))
     23 #define PAD_TO_VALUE(addr, alignment) ((((size_t)addr) + (alignment - 1)) & ~(alignment - 1))
     24
     25 // Calculate the shared buffer size
     26 uint64_t totalBufferSize = 0;
     27 // Calculate the offset of the tensor location in the shared buffer
     28 size_t inputTensorOffsets[numTensors];
     29 for (size_t tensorIdx = 0; tensorIdx < numTensors; tensorIdx++) {
     30 // Calculate the tensorSize based on tensor dimensions and data type
     31 inputTensorOffsets[tensorIdx] = totalBufferSize;
     32 totalBufferSize += tensorSize;
     33 totalBufferSize = MAKE_MULTIPLE(totalBufferSize, sizeAlignment);
     34 }
     35
     36 #define RPCMEM_HEAP_ID_SYSTEM 25
     37 #define RPCMEM_DEFAULT_FLAGS 1
     38
     39 // Allocate the shard buffer
     40 uint8_t* memPointer = (uint8_t*)rpcmem_alloc(RPCMEM_HEAP_ID_SYSTEM, RPCMEM_DEFAULT_FLAGS, totalBufferSize + startAddrAlignment);
     41 if (nullptr == memPointer) {
     42     // handle errors
     43 }
     44 uint8_t* alignedMemPointer = PAD_TO_VALUE(memPointer, startAddrAlignment);
     45
     46 // Get a file descriptor for the buffer
     47 int memFd = rpcmem_to_fd(alignedMemPointer);
     48 if (-1 == memfd) {
     49     // handle errors
     50 }
     51
     52 // Regiter the memory handles using memory descriptors
     53 for (size_t tensorIdx = 0; tensorIdx < numTensors; tensorIdx++) {
     54 // Fill the info of Qnn_MemDescriptor_t and register the descriptor to QNN
     55 // Qnn_MemDescriptor_t is defined in ${QNN_SDK_ROOT}/include/QNN/QnnMem.h
     56 Qnn_MemDescriptor_t memDescriptor;
     57 memDescriptor.memShape = {inputTensors[tensorIdx].rank, inputTensors[tensorIdx].dimensions, nullptr};
     58 memDescriptor.dataType = inputTensors[tensorIdx].dataType;
     59 memDescriptor.memType = QNN_MEM_TYPE_CUSTOM;
     60 inputTensor[tensorIdx].memType = QNN_TENSORMEMTYPE_MEMHANDLE;
     61 inputTensor[tensorIdx].memHandle = nullptr;
     62
     63 // Fill the info of QnnLpaiMem_MemInfoCustom_t and set as custom info
     64 // QnnLpaiMem_MemInfoCustom_t is defined in ${QNN_SDK_ROOT}/include/QNN/LPAI/QnnLpaiMem.h
     65 QnnLpaiMem_MemInfoCustom_t lpaiCustomMemInfo;
     66 lpaiCustomMemInfo.fd = memFd;
     67 lpaiCustomMemInfo.offset = inputTensorOffsets[tensorIdx];
     68
     69 memDescriptor.customInfo = &lpaiCustomMemInfo;
     70
     71 Qnn_ContextHandle_t context; // Must obtain a QNN context handle before memRegister()
     72 // To obtain QNN context handle:
     73 // Refer to ${QNN_SDK_ROOT}/docs/general/sample_app.html#load-context-from-a-cached-binary
     74
     75 Qnn_ErrorHandle_t registRet = qnnInterface->memRegister(context, &memDescriptor, 1u, &(inputTensor[tensorIdx].memHandle));
     76 if (QNN_SUCCESS != registRet) {
     77     // Deregister already created memory handles
     78     rpcmem_free(memPointer);
     79     // handle errors
     80 }
     81 }
     82
     83 /**
     84 * At this place, the allocation and registration of the shared buffer has been complete.
     85 * On QNN side, the buffer has been bound by memfd
     86 * On user side, this buffer can be manipulated through memPointer and offset.
     87 */
     88
     89 /**
     90 * Optionally, user can also allocate and register shared buffer for output as adove codes (lines 26-81).
     91 * And if so the output buffer also should be deregistered and freed as below codes (lines 101-107).
     92 */
     93
     94 // Load the input data to memPointer with respecitve offsets ......
     95
     96 // Execute QNN graph with input tensors and output tensors ......
     97
     98 // Get output data from the memPointer and offset combination ......
     99
    100 // Deregister all mem handles the buffer if it's not being used
    101 for (size_t tensorIdx = 0; tensorIdx < numTensors; tensorIdx++) {
    102     Qnn_ErrorHandle_t deregisterRet = qnnInterface->memDeRegister(&(inputTensors[tensorIdx].memHandle), 1);
    103     if (QNN_SUCCESS != registRet) {
    104     // handle errors
    105     }
    106 }
    107 rpcmem_free(memPointer);
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### Using qnn-net-run with Shared Buffer for Zero-Copy

To enable zero-copy IO tensor allocation when running inference with `qnn-net-run`, pass the
`--shared_buffer` flag. This instructs the tool to allocate graph input and output tensors as
ION shared buffers on Android, enabling direct data exchange between the host CPU and the LPAI
accelerator without memory copies.

Example:

$ ./qnn-net-run \
        --backend ./libQnnLpai.so \
        --retrieve_context ./qnn_model_serialized.bin \
        --input_list ./input_list.txt \
        --shared_buffer
    Copy to clipboard

Note

`--shared_buffer` is supported on Android only. For more details on this option, refer to
qnn-net-run.

## Troubleshooting for QNN LPAI Backends (x86 Simulator, ARM & aDSP)

This paragraph provides a comprehensive guide for executing and troubleshooting QNN LPAI backends across x86 (simulator), ARM (Android), and native aDSP targets.

### Environment Variable Export Commands

#### ARM Target (Android)

export QNN_TARGET_ARCH=aarch64-android
    export HW_VER=v6
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#### aDSP Target (Hexagon DSP)

export QNN_TARGET_ARCH=aarch64-android
    export DSP_ARCH=hexagon-v81
    export DSP_VER=V81
    export HW_VER=v6
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#### x86 Simulator (Linux)

export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:${QNN_SDK_ROOT}/lib/x86_64-linux-clang
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#### x86 Simulator (Windows)

set PATH=%PATH%;%QNN_SDK_ROOT%\lib\x86_64-windows-msvc
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### Execution Examples

#### x86 Simulator (Linux)

From Quantized Model:

cd ${QNN_SDK_ROOT}/examples/QNN/converter/models
    ${QNN_SDK_ROOT}/bin/x86_64-linux-clang/qnn-net-run \
      --backend ${QNN_SDK_ROOT}/lib/x86_64-linux-clang/libQnnLpai.so \
      --model ${QNN_SDK_ROOT}/examples/QNN/example_libs/x86_64-linux-clang/libQnnModel.so \
      --input_list input_list_float.txt \
      --config_file /path/to/config.json
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From Serialized Buffer:

${QNN_SDK_ROOT}/bin/x86_64-linux-clang/qnn-net-run \
      --backend ${QNN_SDK_ROOT}/lib/x86_64-linux-clang/libQnnLpai.so \
      --retrieve_context qnn_model_8bit_quantized.serialized.bin \
      --input_list input_list_float.txt \
      --config_file /path/to/config.json
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#### x86 Simulator (Windows)

cd %QNN_SDK_ROOT%\examples\QNN\converter\models
    %QNN_SDK_ROOT%\bin\x86_64-windows-msvc\qnn-net-run.exe
      --backend %QNN_SDK_ROOT%\lib\x86_64-windows-msvc\QnnLpai.dll
      --model %QNN_SDK_ROOT%\examples\QNN\example_libs\x86_64-windows-msvc\QnnModel.dll
      --input_list input_list_float.txt
      --config_file C:\path\to\config.json
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#### ARM Target (Android)

adb shell mkdir -p /data/local/tmp/LPAI/adsp
    adb push ./output/qnn_model_8bit_quantized.serialized.bin /data/local/tmp/LPAI
    adb push ${QNN_SDK_ROOT}/lib/${QNN_TARGET_ARCH}/libQnnLpai.so /data/local/tmp/LPAI
    adb push ${QNN_SDK_ROOT}/lib/${QNN_TARGET_ARCH}/libQnnLpaiStub.so /data/local/tmp/LPAI
    adb push ${QNN_SDK_ROOT}/lib/${QNN_TARGET_ARCH}/libQnnLpaiNetRunExtensions.so /data/local/tmp/LPAI
    adb push ${QNN_SDK_ROOT}/lib/lpai-${HW_VER}/unsigned/libQnnLpaiSkel.so /data/local/tmp/LPAI/adsp
    adb push ${QNN_SDK_ROOT}/examples/QNN/converter/models/input_data_float /data/local/tmp/LPAI
    adb push ${QNN_SDK_ROOT}/examples/QNN/converter/models/input_list_float.txt /data/local/tmp/LPAI
    adb push ${QNN_SDK_ROOT}/bin/aarch64-android/qnn-net-run /data/local/tmp/LPAI
    
    adb shell
    cd /data/local/tmp/LPAI
    export LD_LIBRARY_PATH=/data/local/tmp/LPAI:/data/local/tmp/LPAI/adsp
    export ADSP_LIBRARY_PATH="/data/local/tmp/LPAI/adsp"
    
    ./qnn-net-run --backend ./libQnnLpai.so --device_options device_id:0 \
      --retrieve_context ./qnn_model_8bit_quantized.serialized.bin \
      --input_list ./input_list_float.txt
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#### aDSP Target (Hexagon DSP)

adb push ${QNN_SDK_ROOT}/lib/lpai-${HW_VER}/unsigned/libQnnLpai.so /data/local/tmp/LPAI/adsp
    adb push ${QNN_SDK_ROOT}/lib/lpai-${HW_VER}/unsigned/libQnnLpaiNetRunExtensions.so /data/local/tmp/LPAI/adsp
    adb push ${QNN_SDK_ROOT}/lib/${DSP_ARCH}/unsigned/libQnnHexagonSkel_App.so /data/local/tmp/LPAI/adsp
    adb push ${QNN_SDK_ROOT}/lib/${DSP_ARCH}/unsigned/libQnnNetRunDirect${DSP_VER}Skel.so /data/local/tmp/LPAI/adsp
    adb push ${QNN_SDK_ROOT}/lib/${QNN_TARGET_ARCH}/libQnnNetRunDirect${DSP_VER}Stub.so /data/local/tmp/LPAI
    
    export LD_LIBRARY_PATH=/data/local/tmp/LPAI:/data/local/tmp/LPAI/adsp
    export ADSP_LIBRARY_PATH="/data/local/tmp/LPAI/adsp"
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#### Example config.json

The table below shows the differences in <cite>config.json</cite> for each supported backend type:

| **Target** | **shared\_library\_path** | **config\_file\_path** | **context\_configs** |
| --- | --- | --- | --- |
| Simulator (x86) | ${QNN\_SDK\_ROOT}/lib/x86\_64-linux-clang/libQnnLpaiNetRunExtensions.so | ./lpaiParams.conf | “is\_persistent\_binary”: false |
| aDSP (Hexagon) | /data/local/tmp/LPAI/adsp/libQnnLpaiNetRunExtensions.so | ./lpaiParams.conf | “is\_persistent\_binary”: true |
| ARM Android | /data/local/tmp/LPAI/libQnnLpaiNetRunExtensions.so | ./lpaiParams.conf | “is\_persistent\_binary”: false |

Each configuration should be wrapped in the following structure:

{
      "backend_extensions": {
        "shared_library_path": "<appropriate path>",
        "config_file_path": "./lpaiParams.conf"
      },
      "context_configs": {
        "is_persistent_binary": true
      }
    }
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## Troubleshooting Table

| **Issue Category** | **Symptoms** | **Resolution & Debugging Commands** |
| --- | --- | --- |
| Library Not Found or Load Errors | <ul class="simple"><br><li><p>cannot locate shared object file</p></li><br><li><p>undefined symbol</p></li><br><li><p>library not found</p></li><br></ul> | <ul class="simple"><br><li><p>Ensure all required <cite>.so</cite> or <cite>.dll</cite> files are present in the correct paths.</p></li><br><li><p>Verify that <cite>LD_LIBRARY_PATH</cite> (Linux) or <cite>PATH</cite> (Windows) includes the correct directories.</p></li><br><li><p>Use <cite>ldd</cite> (Linux) or Dependency Walker (Windows) to inspect missing dependencies.</p><ul><br><li><p>x86 Linux For model generation and Simulatiion:</p><ul><br><li><p><code class="docutils literal notranslate"><span class="pre">${QNN_SDK_ROOT}/lib/x86_64-linux-clang/libQnnLpai.so</span></code></p></li><br><li><p><code class="docutils literal notranslate"><span class="pre">${QNN_SDK_ROOT}/lib/x86_64-linux-clang/libQnnLpaiNetRunExtensions.so</span></code></p></li><br><li><p><code class="docutils literal notranslate"><span class="pre">${QNN_SDK_ROOT}/lib/x86_64-linux-clang/libQnnLpaiPrepare_${HW_VER}.so</span></code></p></li><br><li><p><code class="docutils literal notranslate"><span class="pre">${QNN_SDK_ROOT}/lib/x86_64-linux-clang/libQnnLpaiSim_${HW_VER}.so</span></code></p></li><br></ul><br></li><br><li><p>x86 Windows For model generation and Simulatiion:</p><ul><br><li><p><code class="docutils literal notranslate"><span class="pre">${QNN_SDK_ROOT}/lib/x86_64-windows-msvc/QnnLpai.dll</span></code></p></li><br><li><p><code class="docutils literal notranslate"><span class="pre">${QNN_SDK_ROOT}/lib/x86_64-windows-msvc/QnnLpaiNetRunExtensions.dll</span></code></p></li><br><li><p><code class="docutils literal notranslate"><span class="pre">${QNN_SDK_ROOT}/lib/x86_64-windows-msvc/QnnLpaiPrepare_${HW_VER}.dll</span></code></p></li><br><li><p><code class="docutils literal notranslate"><span class="pre">${QNN_SDK_ROOT}/lib/x86_64-windows-msvc/QnnLpaiSim_${HW_VER}.dll</span></code></p></li><br></ul><br></li><br><li><p>ARM FastRpc Path:</p><ul><br><li><p><code class="docutils literal notranslate"><span class="pre">/data/local/tmp/LPAI/libQnnLpai.so</span></code></p></li><br><li><p><code class="docutils literal notranslate"><span class="pre">/data/local/tmp/LPAI/libQnnLpaiStub.so</span></code></p></li><br><li><p><code class="docutils literal notranslate"><span class="pre">/data/local/tmp/LPAI/libQnnLpaiNetRunExtensions</span></code></p></li><br><li><p><code class="docutils literal notranslate"><span class="pre">/data/local/tmp/LPAI/adsp/libQnnLpaiSkel.so</span></code></p></li><br></ul><br></li><br><li><p>aDSP for LPAI and Hexagon libraries:</p><ul><br><li><p><code class="docutils literal notranslate"><span class="pre">/data/local/tmp/LPAI/adsp/libQnnLpai.so</span></code></p></li><br><li><p><code class="docutils literal notranslate"><span class="pre">/data/local/tmp/LPAI/adsp/libQnnLpaiNetRunExtensions.so</span></code></p></li><br><li><p><code class="docutils literal notranslate"><span class="pre">/data/local/tmp/LPAI/adsp/libQnnHexagonSkel_App.so</span></code></p></li><br><li><p><code class="docutils literal notranslate"><span class="pre">/data/local/tmp/LPAI/adsp/libQnnNetRunDirect${DSP_VER}Skel.so</span></code></p></li><br><li><p><code class="docutils literal notranslate"><span class="pre">/data/local/tmp/LPAI/adsp/libQnnNetRunDirect${DSP_VER}Stub.so</span></code></p></li><br></ul><br></li><br></ul><br></li><br><li><p>Set environment variables:</p><ul><br><li><p>Linux: <code class="docutils literal notranslate"><span class="pre">export</span> <span class="pre">LD_LIBRARY_PATH=$LD_LIBRARY_PATH:${QNN_SDK_ROOT}/lib/x86_64-linux-clang</span></code></p></li><br><li><p>Windows: <code class="docutils literal notranslate"><span class="pre">set</span> <span class="pre">PATH=%PATH%;%QNN_SDK_ROOT%/lib/x86_64-windows-msvc</span></code></p></li><br><li><p>Android: <code class="docutils literal notranslate"><span class="pre">export</span> <span class="pre">LD_LIBRARY_PATH=/data/local/tmp/LPAI:/data/local/tmp/LPAI/adsp</span></code></p></li><br><li><p>Android: <code class="docutils literal notranslate"><span class="pre">export</span> <span class="pre">ADSP_LIBRARY_PATH=&quot;/data/local/tmp/LPAI/adsp&quot;</span></code></p></li><br></ul><br></li><br><li><p>Debug commands:</p><ul><br><li><p><code class="docutils literal notranslate"><span class="pre">ldd</span> <span class="pre">qnn-net-run</span></code> (Linux)</p></li><br><li><p><code class="docutils literal notranslate"><span class="pre">Dependency</span> <span class="pre">Walker</span></code> (Windows)</p></li><br><li><p><code class="docutils literal notranslate"><span class="pre">adb</span> <span class="pre">shell</span> <span class="pre">ls</span> <span class="pre">/data/local/tmp/LPAI</span></code></p></li><br><li><p><code class="docutils literal notranslate"><span class="pre">adb</span> <span class="pre">shell</span> <span class="pre">ls</span> <span class="pre">/data/local/tmp/LPAI/adsp</span></code></p></li><br><li><p><code class="docutils literal notranslate"><span class="pre">readelf</span> <span class="pre">-d</span> <span class="pre">libQnnLpai.so</span></code></p></li><br></ul><br></li><br></ul> |
| Context Binary Generator Setup Failure | <ul class="simple"><br><li><p>qnn-context-binary-generator fails to start</p></li><br><li><p>missing backend or model path</p></li><br><li><p>missing LPAI Prepare library</p></li><br></ul> | <ul class="simple"><br><li><p>Verify qnn-context-binary-generator binary exists:</p><ul><br><li><p>Linux: <code class="docutils literal notranslate"><span class="pre">${QNN_SDK_ROOT}/bin/x86_64-linux-clang/qnn-context-binary-generator</span></code></p></li><br><li><p>Windows: <code class="docutils literal notranslate"><span class="pre">${QNN_SDK_ROOT}/bin/x86_64-windows-msvc/qnn-context-binary-generator.exe</span></code></p></li><br></ul><br></li><br><li><p>Verify Prepare library exists:</p><ul><br><li><p>Linux: <code class="docutils literal notranslate"><span class="pre">${QNN_SDK_ROOT}/lib/x86_64-linux-clang/libQnnLpaiPrepare_${HW_VER}.so</span></code></p></li><br><li><p>Windows: <code class="docutils literal notranslate"><span class="pre">${QNN_SDK_ROOT}/lib/x86_64-windows-msvc/QnnLpaiPrepare_${HW_VER}.so</span></code></p></li><br></ul><br></li><br><li><p>Check backend and model paths are correct</p></li><br><li><p>Debug commands:</p><ul><br><li><p><code class="docutils literal notranslate"><span class="pre">which</span> <span class="pre">qnn-context-binary-generator</span></code></p></li><br><li><p><code class="docutils literal notranslate"><span class="pre">ls</span> <span class="pre">-l</span> <span class="pre">${QNN_SDK_ROOT}/bin/x86_64-linux-clang/</span></code></p></li><br><li><p><code class="docutils literal notranslate"><span class="pre">ls</span> <span class="pre">-l</span> <span class="pre">${QNN_SDK_ROOT}/lib/x86_64-linux-clang/</span></code></p></li><br></ul><br></li><br></ul> |
| Simulator Setup Failure | <ul class="simple"><br><li><p>qnn-net-run fails to start</p></li><br><li><p>missing backend or model path</p></li><br><li><p>missing LPAI Simulation library</p></li><br></ul> | <ul class="simple"><br><li><p>Verify qnn-net-run binary exists:</p><ul><br><li><p>Linux: <code class="docutils literal notranslate"><span class="pre">${QNN_SDK_ROOT}/bin/x86_64-linux-clang/qnn-net-run</span></code></p></li><br><li><p>Windows: <code class="docutils literal notranslate"><span class="pre">${QNN_SDK_ROOT}/bin/x86_64-windows-msvc/qnn-net-run.exe</span></code></p></li><br></ul><br></li><br><li><p>Verify simulation library exists:</p><ul><br><li><p>Linux: <code class="docutils literal notranslate"><span class="pre">${QNN_SDK_ROOT}/lib/x86_64-linux-clang/libQnnLpaiSim_${HW_VER}.so</span></code></p></li><br><li><p>Windows: <code class="docutils literal notranslate"><span class="pre">${QNN_SDK_ROOT}/lib/x86_64-windows-msvc/QnnLpaiSim_${HW_VER}.so</span></code></p></li><br></ul><br></li><br><li><p>Check backend and model paths are correct</p></li><br><li><p>Debug commands:</p><ul><br><li><p><code class="docutils literal notranslate"><span class="pre">which</span> <span class="pre">qnn-net-run</span></code></p></li><br><li><p><code class="docutils literal notranslate"><span class="pre">ls</span> <span class="pre">-l</span> <span class="pre">${QNN_SDK_ROOT}/bin/x86_64-linux-clang/</span></code></p></li><br><li><p><code class="docutils literal notranslate"><span class="pre">ls</span> <span class="pre">-l</span> <span class="pre">${QNN_SDK_ROOT}/lib/x86_64-linux-clang/</span></code></p></li><br></ul><br></li><br></ul> |
| Incorrect Environment Variables | <ul class="simple"><br><li><p>backend fails to load</p></li><br><li><p>ADSP libraries not found</p></li><br></ul> | <ul class="simple"><br><li><p>Ensure correct exports are set, see example for v5 (see Supported Platform for more info):</p><ul><br><li><p><code class="docutils literal notranslate"><span class="pre">export</span> <span class="pre">QNN_TARGET_ARCH=aarch64-android</span></code></p></li><br><li><p><code class="docutils literal notranslate"><span class="pre">export</span> <span class="pre">DSP_ARCH=hexagon-v79</span></code></p></li><br><li><p><code class="docutils literal notranslate"><span class="pre">export</span> <span class="pre">DSP_VER=V79</span></code></p></li><br><li><p><code class="docutils literal notranslate"><span class="pre">export</span> <span class="pre">HW_VER=v5</span></code></p></li><br></ul><br></li><br><li><p>Debug commands:</p><ul><br><li><p><code class="docutils literal notranslate"><span class="pre">echo</span> <span class="pre">$QNN_TARGET_ARCH</span></code></p></li><br><li><p><code class="docutils literal notranslate"><span class="pre">env</span> <span class="pre">|</span> <span class="pre">grep</span> <span class="pre">DSP</span></code></p></li><br></ul><br></li><br></ul> |
| ELF Format or Architecture Mismatch | <ul class="simple"><br><li><p>Exec format error</p></li><br><li><p>wrong ELF class</p></li><br><li><p>no such file (even though the file exists)</p></li><br></ul> | <ul class="simple"><br><li><p>Use <cite>file &lt;binary&gt;</cite> to inspect architecture compatibility.</p><ul><br><li><p><code class="docutils literal notranslate"><span class="pre">file</span> <span class="pre">libQnnLpai.so</span></code> → should show correct ELF type for target platform</p></li><br></ul><br></li><br><li><p>Ensure binaries match target architecture:</p><ul><br><li><p><strong>Simulator (x86 Linux)</strong>: <code class="docutils literal notranslate"><span class="pre">ELF</span> <span class="pre">64-bit</span> <span class="pre">LSB</span> <span class="pre">shared</span> <span class="pre">object,</span> <span class="pre">x86-64</span></code></p><ul><br><li><p><code class="docutils literal notranslate"><span class="pre">${QNN_SDK_ROOT}/lib/x86_64-linux-clang/libQnnLpai.so</span></code></p></li><br><li><p><code class="docutils literal notranslate"><span class="pre">${QNN_SDK_ROOT}/lib/x86_64-linux-clang/libQnnLpaiNetRunExtensions.so</span></code></p></li><br><li><p><code class="docutils literal notranslate"><span class="pre">${QNN_SDK_ROOT}/bin/x86_64-linux-clang/qnn-net-run</span></code></p></li><br></ul><br></li><br><li><p><strong>Simulator (x86 Windows)</strong>: PE32/PE32+ DLLs and EXEs</p><ul><br><li><p><code class="docutils literal notranslate"><span class="pre">${QNN_SDK_ROOT}/lib/x86_64-windows-msvc/QnnLpai.dll</span></code></p></li><br><li><p><code class="docutils literal notranslate"><span class="pre">${QNN_SDK_ROOT}/lib/x86_64-windows-msvc/libQnnLpaiNetRunExtensions.dll</span></code></p></li><br><li><p><code class="docutils literal notranslate"><span class="pre">${QNN_SDK_ROOT}/bin/x86_64-windows-msvc/qnn-net-run.exe</span></code></p></li><br></ul><br></li><br><li><p><strong>ARM (Android)</strong>: <code class="docutils literal notranslate"><span class="pre">ELF</span> <span class="pre">64-bit</span> <span class="pre">LSB</span> <span class="pre">shared</span> <span class="pre">object,</span> <span class="pre">ARM</span> <span class="pre">aarch64</span></code></p><ul><br><li><p><code class="docutils literal notranslate"><span class="pre">/data/local/tmp/LPAI/libQnnLpai.so</span></code></p></li><br><li><p><code class="docutils literal notranslate"><span class="pre">/data/local/tmp/LPAI/libQnnLpaiNetRunExtensions.so</span></code></p></li><br><li><p><code class="docutils literal notranslate"><span class="pre">/data/local/tmp/LPAI/qnn-net-run</span></code></p></li><br></ul><br></li><br><li><p><strong>aDSP (Hexagon)</strong>: <code class="docutils literal notranslate"><span class="pre">ELF</span> <span class="pre">32-bit</span> <span class="pre">LSB</span> <span class="pre">shared</span> <span class="pre">object,</span> <span class="pre">Hexagon</span></code></p><ul><br><li><p><code class="docutils literal notranslate"><span class="pre">/data/local/tmp/LPAI/adsp/libQnnLpai.so</span></code></p></li><br><li><p><code class="docutils literal notranslate"><span class="pre">/data/local/tmp/LPAI/adsp/libQnnLpaiNetRunExtensions.so</span></code></p></li><br></ul><br></li><br></ul><br></li><br><li><p>Debug commands:</p><ul><br><li><p><code class="docutils literal notranslate"><span class="pre">readelf</span> <span class="pre">-h</span> <span class="pre">libQnnLpai.so</span></code></p></li><br></ul><br></li><br></ul> |
| Unsigned or Improperly Signed Libraries | <ul class="simple"><br><li><p>backend fails silently</p></li><br><li><p>model fails to execute</p></li><br></ul> | <ul class="simple"><br><li><p>Use Qualcomm signing tools (<cite>sectools</cite>, <cite>sign_hexagon.py</cite>) to sign required libraries.</p></li><br><li><p>Ensure signed libraries are pushed to <cite>/data/local/tmp/LPAI/adsp</cite>.</p></li><br><li><p>Sign required libraries using Qualcomm tools:</p><ul><br><li><p>Tools: <code class="docutils literal notranslate"><span class="pre">sectools</span></code>, <code class="docutils literal notranslate"><span class="pre">sign_hexagon.py</span></code></p></li><br></ul><br></li><br><li><p>For ARM (Android):</p><ul><br><li><p>Sign and push to <code class="docutils literal notranslate"><span class="pre">/data/local/tmp/LPAI/adsp/</span></code>:</p><ul><br><li><p><code class="docutils literal notranslate"><span class="pre">${QNN_SDK_ROOT}/lib/lpai-${HW_VER}/unsigned/libQnnLpaiSkel.so</span></code></p></li><br></ul><br></li><br></ul><br></li><br><li><p>For aDSP (native DSP):</p><ul><br><li><p>Sign and push to <code class="docutils literal notranslate"><span class="pre">/data/local/tmp/LPAI/adsp/</span></code>:</p><ul><br><li><p><code class="docutils literal notranslate"><span class="pre">${QNN_SDK_ROOT}/lib/lpai-${HW_VER}/unsigned/libQnnLpai.so</span></code></p></li><br><li><p><code class="docutils literal notranslate"><span class="pre">${QNN_SDK_ROOT}/lib/lpai-${HW_VER}/unsigned/libQnnLpaiNetRunExtensions.so</span></code></p></li><br><li><p><code class="docutils literal notranslate"><span class="pre">${QNN_SDK_ROOT}/lib/${DSP_ARCH}/unsigned/libQnnHexagonSkel_App.so</span></code></p></li><br><li><p><code class="docutils literal notranslate"><span class="pre">${QNN_SDK_ROOT}/lib/${DSP_ARCH}/unsigned/libQnnNetRunDirect${DSP_VER}Skel.so</span></code></p></li><br></ul><br></li><br></ul><br></li><br><li><p>Debug commands:</p><ul><br><li><p><code class="docutils literal notranslate"><span class="pre">adb</span> <span class="pre">shell</span> <span class="pre">ls</span> <span class="pre">-l</span> <span class="pre">/data/local/tmp/LPAI/adsp/</span></code></p></li><br></ul><br></li><br></ul> |
| Invalid or Missing Configuration | <ul class="simple"><br><li><p>config.json not found</p></li><br><li><p>backend extension fails</p></li><br></ul> | <ul><br><li><p>Ensure <cite>config.json</cite> and <cite>lpaiParams.conf</cite> are present and valid.</p></li><br><li><dl class="simple"><br><dt>Validate JSON syntax and required fields</dt><dd><p>(<cite>shared_library_path</cite>, <cite>config_file_path</cite>, <cite>is_persistent_binary</cite>).</p><br></dd><br></dl><ul class="simple"><br><li><p>Example config.json:</p></li><br></ul><br></li><br></ul><br><br>{<br>      "backend_extensions": {<br>        "shared_library_path": "/data/local/tmp/LPAI/adsp/libQnnLpaiNetRunExtensions.so",<br>        "config_file_path": "./lpaiParams.conf"<br>      }<br>    }<br>    Copy to clipboard |
| Model Execution Fails or Incorrect Output | <ul class="simple"><br><li><p>model crashes</p></li><br><li><p>Output is incorrect or empty</p></li><br></ul> | <ul class="simple"><br><li><p>Verify QNN model was quantized and serialized correctly</p></li><br><li><p>Ensure input data matches expected format and dimensions.</p></li><br><li><p>Check for version mismatches between model and runtime.</p></li><br><li><p>Confirm you dont see message like: <code class="docutils literal notranslate"><span class="pre">version</span> <span class="pre">4.xx</span> <span class="pre">is</span> <span class="pre">not</span> <span class="pre">supported</span> <span class="pre">on</span> <span class="pre">runtime</span> <span class="pre">5.xx</span></code></p></li><br><li><p>Debug commands:</p><ul><br><li><p><code class="docutils literal notranslate"><span class="pre">qnn-net-run</span> <span class="pre">--log_level</span> <span class="pre">debug</span> <span class="pre">&lt;add</span> <span class="pre">your</span> <span class="pre">other</span> <span class="pre">previous</span> <span class="pre">options</span> <span class="pre">here&gt;</span></code></p></li><br><li><p><code class="docutils literal notranslate"><span class="pre">adb</span> <span class="pre">logcat</span> <span class="pre">|</span> <span class="pre">grep</span> <span class="pre">supported</span></code></p></li><br></ul><br></li><br></ul> |
| Root Permissions | <ul class="simple"><br><li><p>permission denied errors</p></li><br><li><p>cannot access /data/local/tmp</p></li><br></ul> | <ul class="simple"><br><li><p>Ensure commands are run with root privileges:</p><ul><br><li><p>Use <code class="docutils literal notranslate"><span class="pre">adb</span> <span class="pre">root</span></code> and <code class="docutils literal notranslate"><span class="pre">adb</span> <span class="pre">remount</span></code> before pushing files</p></li><br><li><p>Use <code class="docutils literal notranslate"><span class="pre">su</span></code> or <code class="docutils literal notranslate"><span class="pre">sudo</span></code> where applicable</p></li><br></ul><br></li><br><li><p>Debug commands:</p><ul><br><li><p><code class="docutils literal notranslate"><span class="pre">adb</span> <span class="pre">shell</span> <span class="pre">whoami</span></code></p></li><br><li><p><code class="docutils literal notranslate"><span class="pre">ls</span> <span class="pre">-l</span> <span class="pre">/data/local/tmp</span></code></p></li><br></ul><br></li><br></ul> |

## QNN LPAI Backend FAQs

**How do I create handles for QNN components?**

To initialize QNN components, the following APIs must be used:

- `QnnBackend_create()`: Instantiates the LPAI backend handle, which manages backend-specific operations and resources.
- `QnnSystemContext_create()`: Creates the QNN system context handle, responsible for managing the graph lifecycle, context metadata, and execution environment.

These handles are foundational for interacting with the QNN runtime and must be created before any graph execution or profiling.

**What does it mean if ``QnnInterface\_getProviders()`` returns zero providers?**

A return value of zero providers typically indicates that the backend libraries are either missing, not properly installed, or not discoverable by the runtime.

To resolve this issue:

- Ensure that the required backend shared libraries (e.g., `libQnnLpai.so`) are present on the target system.
- Verify that the environment variable `LD_LIBRARY_PATH` includes the directory containing the QNN backend libraries.
- Confirm that the backend ID `QNN_LPAI_BACKEND_ID` is correctly specified when querying providers.

**Why is buffer alignment important?**

Proper buffer alignment is essential to ensure correct execution and compatibility with the LPAI backend. Misaligned buffers can lead to invalid memory access and runtime errors, especially when interfacing with hardware accelerators that enforce strict alignment constraints.

To determine alignment requirements:

- Use `QnnBackend_getProperty()` with the property `QNN_LPAI_BACKEND_GET_PROP_ALIGNMENT_REQ`.
- This query returns:

    - **Start Address Alignment**: Specifies the required alignment for the base address of each buffer.
    - **Buffer Size Alignment**: Specifies the required alignment for the total size of each buffer.

**What are the consequences of not meeting alignment requirements?**

Failure to comply with alignment constraints may result in:

- Application crashes due to invalid or misaligned memory access.
- Backend API errors during buffer registration or graph execution.
- Incorrect or undefined inference results due to improper memory handling.

It is strongly recommended to query and apply alignment requirements before allocating memory for input, output, or intermediate buffers.

**Can multiple backends be used concurrently?**

No. QNN supports only one backend per context. Each context is tightly coupled with a single backend implementation.

To use multiple backends within the same application:

- Create separate QNN contexts for each backend.
- Ensure that each context is independently initialized and managed.

**Can graphs be modified after context finalization?**

No. Once a context is created from a binary using `QnnContext_createFromBinary()`, it becomes immutable. This means:

- The graph structure, layers, and parameters cannot be modified.
- Any changes to the model require regenerating the context binary and reinitializing the context.

This immutability ensures consistency and performance optimization during inference.

**Is ``QnnGraph\_execute()`` a blocking call?**

Yes. The `QnnGraph_execute()` API is synchronous and blocking. It will not return control to the caller until the entire graph execution is complete.

- This behavior ensures deterministic execution and simplifies synchronization.
- If asynchronous execution is required, it must be implemented at the application level using separate threads or processes.

**Where is the output stored after execution?**

Output data is written to the client-provided output buffers that were registered during initialization. These buffers must:

- Be properly allocated and aligned according to backend requirements.
- Remain valid and accessible throughout the execution lifecycle.

The application is responsible for managing the lifecycle and memory of these buffers.

**Is the order of deinitialization important?**

Yes. Resources must be released in the reverse order of their allocation to avoid dependency violations or memory access errors.

Recommended deinitialization order:

1. Release graph and context resources.
2. Destroy the system context handle.
3. Destroy the backend handle.

Improper deinitialization may result in memory leaks, dangling pointers, or undefined behavior.

**Can a context be reused after deinitialization?**

No. Once a context is released using `QnnContext_free()`, it is no longer valid and cannot be reused.

- To execute the same model again, the context must be recreated using `QnnContext_createFromBinary()`.
- Ensure that all associated resources are reinitialized as needed.

## QNN LPAI Backend Glossary

- LPAI
    - Low Power AI backend optimized for low-area, low-power applications such as always-on voice and camera use cases.

- QNN\_LPAI\_API\_VERSION\_MAJOR
    - Major version of the QNN LPAI backend API. Current: .

- QNN\_LPAI\_API\_VERSION\_MINOR
    - Minor version of the QNN LPAI backend API. Current: .

- QNN\_LPAI\_API\_VERSION\_PATCH
    - Patch version of the QNN LPAI backend API. Current: .

- QNN\_SDK\_ROOT
    - Environment variable pointing to the root directory of the QNN SDK installation.

- LD\_LIBRARY\_PATH
    - Linux environment variable specifying paths to shared libraries required by QNN tools.

- target\_env
    - Specifies the target environment for model execution. Options: <cite>arm</cite>, <cite>adsp</cite>, <cite>x86</cite>.

- enable\_hw\_ver
    - Specifies the hardware version of the LPAI backend. Options: <cite>v5</cite>, <cite>v5_1</cite>, <cite>v6</cite>.

- fps
    - Frames per second setting for model execution. Default: 1.

- ftrt\_ratio
    - Frame-to-real-time ratio. Default: 10.

- client\_type
    - Type of workload. Options: <cite>real_time</cite>, <cite>non_real_time</cite>.

- affinity
    - Core affinity policy. Options: <cite>soft</cite>, <cite>hard</cite>.

- core\_selection
    - Specifies the core number for execution. Default: 0.

- profiling\_level
    - Level of profiling detail. Options: <cite>basic</cite>, <cite>detailed</cite>.

- is\_persistent\_binary
    - Indicates whether the context binary must persist until <cite>QnnContext_free</cite> is called.

- QnnContext\_createFromBinary
    - API used to create a QNN context from a serialized binary.

- QnnGraph\_finalize
    - API used to finalize a graph before execution.

- QnnGraph\_execute
    - API used to execute a finalized graph.

- QnnContext\_free
    - API used to release a QNN context.

- QnnLpaiMem\_MemType
    - Enum defining memory types: <cite>DDR</cite>, <cite>LLC</cite>, <cite>TCM</cite>, <cite>UNDEFINED</cite>.

- QnnLpaiGraph\_ClientPerfType
    - Enum defining client performance types: <cite>REAL_TIME</cite>, <cite>NON_REAL_TIME</cite>.

- QnnLpaiGraph\_CoreAffinityType
    - Enum defining core affinity types: <cite>SOFT</cite>, <cite>HARD</cite>, <cite>UNDEFINED</cite>.

- Scratch Memory
    - Memory used for intermediate results that can be overwritten during execution.

- Persistent Memory
    - Memory used for intermediate results that must persist across operations.

- Backend Extension
    - JSON configuration enabling custom options for LPAI backend tools.

- qnn-net-run
    - Tool used to execute QNN models on supported platforms.

- qnn-context-binary-generator
    - Tool used to generate offline context binaries for QNN models.

- qnn-profile-viewer
    - Tool used to visualize the profiling data.

- QNN\_CONVERTOR
    - Tool responsible for converting and quantizing models to QNN format.

Last Published: Jun 04, 2026

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